Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions. Sentiment or opinion analysis has of late emerged one of the most
researched and talked about subject in Natural Language Processing (NLP), thanks
mainly to sites like Twitter. In the past, sentiment analysis models using Twitter data have
been built to predict sales performance, rank products and merchants, public opinion
polls, predict election results, political standpoints, predict box-office revenues for movies
and even predict the stock market. This study proposes a general frame in R programming
language to act as a gateway for the analysis of the tweets that portray emotions in a
short and concentrated format. The target tweets include brief emotion descriptions and
words that are not used with a proper format or grammatical structure. Majority of the
work constituted in Turkish includes the data scope and the aim of preparing a data-set.
There is no concrete and usable work done on Turkish Tweet sentiment analysis as a
software client/web application. This study is a starting point on building up the next
steps. The aim is to compare five different common machine learning methods (support
vector machines, random forests, boosting, maximum entropy, and artificial neural
networks) to classify Twitters sentiments
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
"Knowing about the user’s feedback can come to a greater aid in knowing the user as well as improving the organization. Here an example of student’s data is taken for study purpose. Analyzing the student feedback will help to help to address student related problems and help to make teaching more student oriented. Prashali S. Shinde | Asmita R. Kanase | Rutuja S. Pawar | Yamini U. Waingankar ""Sentiment Analysis of Feedback Data"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23090.pdf
Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/23090/sentiment-analysis-of-feedback-data/prashali--s-shinde"
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
"Knowing about the user’s feedback can come to a greater aid in knowing the user as well as improving the organization. Here an example of student’s data is taken for study purpose. Analyzing the student feedback will help to help to address student related problems and help to make teaching more student oriented. Prashali S. Shinde | Asmita R. Kanase | Rutuja S. Pawar | Yamini U. Waingankar ""Sentiment Analysis of Feedback Data"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Fostering Innovation, Integration and Inclusion Through Interdisciplinary Practices in Management , March 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23090.pdf
Paper URL: https://www.ijtsrd.com/other-scientific-research-area/other/23090/sentiment-analysis-of-feedback-data/prashali--s-shinde"
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Due to the fast growth of World Wide Web the online communication has increased. In recent times the communication focus has shifted to social networking. In order to enhance the text methods of communication such as tweets, blogs and chats, it is necessary to examine the emotion of user by studying the input text. Online reviews are posted by customers for the products and services on offer at a website portal. This has provided impetus to substantial growth of online purchasing making opinion analysis a vital factor for business development. To analyze such text and reviews sentiment analysis is used. Sentiment analysis is a sub domain of Natural Language Processing which acquires writer’s feelings about several products which are placed on the internet through various comments or posts. It is used to find the opinion or response of the user. Opinion may be positive, negative or neutral. In this paper a review on sentiment analysis is done and the challenges and issues involved in the process are discussed. The approaches to sentiment analysis using dictionaries such as SenticNet, SentiFul, SentiWordNet, and WordNet are studied. Dictionary-based approaches are efficient over a domain of study. Although a generalized dictionary like WordNet may be used, the accuracy of the classifier get affected due to issues like negation, synonyms, sarcasm, etc.
w
Sentiment classification for product reviews (documentation)Mido Razaz
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
Sentiment Analysis on Twitter Dataset using R Languageijtsrd
Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi
A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document IJECEIAES
Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.
Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Re...Dr. Amarjeet Singh
Any E-Commerce website gets bad reputation if they
sell a product which has bad review, the user blames the eCommerce website rather than manufacturers most of the
times. In some review sites some great audits are included by
the item organization individuals itself so as to make so as to
deliver false positive item reviews. To eliminate these type of
fake product review, we will create a system that finds out the
fake reviews and eliminates all the fake reviews by using
machine learning. We also remove the reviews that are flood
by a marketing agency in order to boost up the ratings of a
particular product .Finally Sentiment analysis is done for the
genuine reviews to classify them into positive and negative.
We will use Bag-of-words to label individual words
according to their sentiment.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
Book recommendation system using opinion mining techniqueeSAT Journals
Abstract
The purpose of this project is to create and deploy a book recommendation system that will help people to recommend books. Our project is the online system that helps people to get reviews about the books and give recommendations to them. Online recommendation system will also allow the users to give feedback comments that will be analyzed by opinion mining technique so as to imply the true nature of the comment .i .e whether the comment is positive, negative or a neutral one. People then searching for a particular book will be displayed with the top 10(approx.) books on that particular subject based on the reviews and feedbacks given by the earlier people who read the same book.
Keywords: - Books, Recommendation, User reviews, Opinion mining, Feedback
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to
sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available
in digital form. One important problem in sentiment analysis of product reviews is to produce summary of
opinions based on product features. We have surveyed and analyzed in this paper, various techniques that
have been developed for the key tasks of opinion mining. We have provided an overall picture of what is
involved in developing a software system for opinion mining on the basis of our survey and analysis.
A Baseline Based Deep Learning Approach of Live Tweetsijtsrd
In this scenario social media plays a vital role in influencing the life of people. Twitter , Facebook, Instagram etc are the major social media platforms . They act as a platform for users to raise their opinions on things and events around them. Twitter is one such micro blogging site that allows the user to tweet 6000 tweets per day each of 280 characters long. Data analyst rely on this data to reach conclusion on the events happening around and also to rate a product. But due to massive volume of reviews the analysts find it difficult to go through them and reach at conclusions. In order to solve this problem we adopt the method of sentiment analysis. Sentiment analysis is an approach to classify the sentiment of user reviews, documents etc in terms of positive good , negative bad , neutral surprise . I suggest an enhanced twitter sentiment analysis that retrieves data based on a baseline in a particular pre defined time span and performs sentiment analysis using Textblob . This scheme differs from the traditional and existing one which performs sentiment analysis on pre saved data by performing sentiment analysis on real time data fetched via Twitter API . Thereby providing a much recent and relevant conclusion. Anjana Jimmington ""A Baseline Based Deep Learning Approach of Live Tweets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23918.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23918/a-baseline-based-deep-learning-approach-of-live-tweets/anjana-jimmington
Due to the fast growth of World Wide Web the online communication has increased. In recent times the communication focus has shifted to social networking. In order to enhance the text methods of communication such as tweets, blogs and chats, it is necessary to examine the emotion of user by studying the input text. Online reviews are posted by customers for the products and services on offer at a website portal. This has provided impetus to substantial growth of online purchasing making opinion analysis a vital factor for business development. To analyze such text and reviews sentiment analysis is used. Sentiment analysis is a sub domain of Natural Language Processing which acquires writer’s feelings about several products which are placed on the internet through various comments or posts. It is used to find the opinion or response of the user. Opinion may be positive, negative or neutral. In this paper a review on sentiment analysis is done and the challenges and issues involved in the process are discussed. The approaches to sentiment analysis using dictionaries such as SenticNet, SentiFul, SentiWordNet, and WordNet are studied. Dictionary-based approaches are efficient over a domain of study. Although a generalized dictionary like WordNet may be used, the accuracy of the classifier get affected due to issues like negation, synonyms, sarcasm, etc.
w
Sentiment classification for product reviews (documentation)Mido Razaz
The documentation of the pre-master graduation project prepared by my self and my colleagues Mostafa Ameen, Mai M. Farag and Mohamed Abd El kader.
If you want me to conduct any similar research for you you can have my service through this link: https://www.fiverr.com/meizzo/convert-your-textual-data-set-from-csv-file-format-to-arff-format-for-weka
Sentiment Analysis on Twitter Dataset using R Languageijtsrd
Sentiment Analysis involves determining the evaluative nature of a piece of text. A product review can express a positive, negative, or neutral sentiment or polarity . Automatically identifying sentiment expressed in text has a number of applications, including tracking sentiment towards Movie reviews and Automobile reviews improving customer relation models, detecting happiness and well being, and improving automatic dialogue systems. The evaluative intensity for both positive and negative terms changes in a negated context, and the amount of change varies from term to term. To adequately capture the impact of negation on individual terms, here proposed to empirically estimate the sentiment scores of terms in negated context from movie review and auto mobile review, and built two lexicons, one for terms in negated contexts and one for terms in affirmative non negated contexts. By using these Affirmative Context Lexicons and Negated Context Lexicons were able to significantly improve the performance of the overall sentiment analysis system on both tasks. This thesis have proposed a sentiment analysis system that detects the sentiment of corpus dataset using movie review and Automobile review as well as the sentiment of a term a word or a phrase within a message term level task using R language. B. Nagajothi | Dr. R. Jemima Priyadarsini "Sentiment Analysis on Twitter Dataset using R Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-6 , October 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28071.pdf Paper URL: https://www.ijtsrd.com/computer-science/data-miining/28071/sentiment-analysis-on-twitter-dataset-using-r-language/b-nagajothi
A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document IJECEIAES
Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset.
Fake Product Review Monitoring & Removal and Sentiment Analysis of Genuine Re...Dr. Amarjeet Singh
Any E-Commerce website gets bad reputation if they
sell a product which has bad review, the user blames the eCommerce website rather than manufacturers most of the
times. In some review sites some great audits are included by
the item organization individuals itself so as to make so as to
deliver false positive item reviews. To eliminate these type of
fake product review, we will create a system that finds out the
fake reviews and eliminates all the fake reviews by using
machine learning. We also remove the reviews that are flood
by a marketing agency in order to boost up the ratings of a
particular product .Finally Sentiment analysis is done for the
genuine reviews to classify them into positive and negative.
We will use Bag-of-words to label individual words
according to their sentiment.
With the rapid growth in ecommerce, reviews for popular products on the web have grown rapidly.
Although these reviews are important for making decisions, it is difficult to read all the reviews.
Automating the opinion mining process was identified as a solution for the problem. Although there are
algorithms for opinion mining, an algorithm with better accuracy is needed. A feature and smiley based
algorithm was developed which extracts product features from reviews based on feature frequency and
generates an opinion summary based on product features.
The algorithm was tested on downloaded customer reviews. The sentences were tagged, opinion words
were extracted and opinion orientations were identified using semantic orientation of opinion words and
smileys. Since the precision values for feature extraction and both precision and recall values for opinion
orientation identification were improved by the new algorithm, it is more successful in opinion mining of
customer reviews.
Book recommendation system using opinion mining techniqueeSAT Journals
Abstract
The purpose of this project is to create and deploy a book recommendation system that will help people to recommend books. Our project is the online system that helps people to get reviews about the books and give recommendations to them. Online recommendation system will also allow the users to give feedback comments that will be analyzed by opinion mining technique so as to imply the true nature of the comment .i .e whether the comment is positive, negative or a neutral one. People then searching for a particular book will be displayed with the top 10(approx.) books on that particular subject based on the reviews and feedbacks given by the earlier people who read the same book.
Keywords: - Books, Recommendation, User reviews, Opinion mining, Feedback
Opinion mining on newspaper headlines using SVM and NLPIJECEIAES
Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.
The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to
sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available
in digital form. One important problem in sentiment analysis of product reviews is to produce summary of
opinions based on product features. We have surveyed and analyzed in this paper, various techniques that
have been developed for the key tasks of opinion mining. We have provided an overall picture of what is
involved in developing a software system for opinion mining on the basis of our survey and analysis.
A Baseline Based Deep Learning Approach of Live Tweetsijtsrd
In this scenario social media plays a vital role in influencing the life of people. Twitter , Facebook, Instagram etc are the major social media platforms . They act as a platform for users to raise their opinions on things and events around them. Twitter is one such micro blogging site that allows the user to tweet 6000 tweets per day each of 280 characters long. Data analyst rely on this data to reach conclusion on the events happening around and also to rate a product. But due to massive volume of reviews the analysts find it difficult to go through them and reach at conclusions. In order to solve this problem we adopt the method of sentiment analysis. Sentiment analysis is an approach to classify the sentiment of user reviews, documents etc in terms of positive good , negative bad , neutral surprise . I suggest an enhanced twitter sentiment analysis that retrieves data based on a baseline in a particular pre defined time span and performs sentiment analysis using Textblob . This scheme differs from the traditional and existing one which performs sentiment analysis on pre saved data by performing sentiment analysis on real time data fetched via Twitter API . Thereby providing a much recent and relevant conclusion. Anjana Jimmington ""A Baseline Based Deep Learning Approach of Live Tweets"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23918.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/23918/a-baseline-based-deep-learning-approach-of-live-tweets/anjana-jimmington
TOWARDS AUTOMATIC DETECTION OF SENTIMENTS IN CUSTOMER REVIEWSijistjournal
Opinions Play important role in the process of knowledge discovery or information retrieval and can be considered as a sub discipline of Data Mining. A major interest has been received towards the automatic extraction of human opinions from web documents. The sole purpose of Sentiment Analysis is to facilitate online consumers in decision making process of purchasing new products. Opinion Mining deals with searching of sentiments that are expressed by Individuals through on-line reviews,surveys, feedback,personal blogs etc. With the vast increase in the utilization of Internet in today's era a similar increase has been seen in the use of blog's,reviews etc. The person who actually uses these reviews or blog's is mostly a consumer or a manufacturer. As most of the customers of the world are buying & selling product on-line so it becomes company's responsibility to make their product updated. In the current scenario companies are taking product reviews from the customers and on the basis of product reviews they are able to know in which they are lacking or strong this can be accomplished with the help of sentiment analysis. Therefore Our objective of our research is to build a tool which can automatically extract opinion words and find out their polarity by using dictionary,This actually reduces the manual effort of reading these reviews and to evaluate them. The research also illustrates the benefits of using Unstructured text instead of training data which expensive . In this research effort we demonstrate a method which is based on rules where product reviews are extracted from review containing sites and analysis is done, so that a person may know whether a particular product review is positive or negative or neutral. The system will utilize a existing knowledge base for calculate positive and negative scores and on the basis of that decide whether a product is recommended or not. The system will evaluate the utility of Lexical resources over the training data.
A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
A Corpus Driven, Aspect-based Sentiment Analysis To Evaluate In Almost Real-t...CSCJournals
Nowadays, more than ever, customers have access to other consumers’ digital evaluations concerning the products or services that they have consumed. The use of online review websites, by the potential digital consumers, makes them aware of the choices they have. This, enables them to make comparisons between all the available products or services. However, the big volume of the opinionative data that is produced continuously, creates difficulties when being analyzed by stakeholders, mostly due to human’s physical or mental restrictions. In this research, web scraping combined with an aspect-level sentiment analysis using the corpus-based technique, approached methodologically the problem, by identifying not only the relevant information, but also the particular expressions and phrases that the reviewers use over the Internet. The purpose is to recommend a corpus-based, sentiment analysis web system for detecting and quantifying customers’ opinions which are written in Greek language and referred to the Food and Beverage (F&B) sector in almost real-time. The system consists of two modules that constructed using the aforementioned methods. As far as the web scraping module is concerned, the BeautifulSoup and the Requests libraries of Python programming language were used. For the constructing purposes of the corpus-based sentiment analysis module, 80,500 customers’ reviews are extracted (data set) from 6,795 companies which selected randomly from the most popular Greek e-ordering platform. The evaluated functions are the quality of food, the customer service and the image of the company. The extracted sentiment orientation terms and phrases from the customers’ reviews are used to form the corresponding dictionaries of the functions and the appropriate pattern of tags, in order to proceed in the sentiment classification. Finally, the system is tested in the dataset and the findings will be practical and significant, as not enough attention has been paid in sentiment analysis techniques used in combination with a non-English, like the modern Greek language.
Twitter Sentiment Analysis Project Done using R.
In these Project we deal with the tweets database that are avaialble to us by the Twitter. We clean the tweets and break them out into tokens and than analysis each word using Bag of Word concept and than rate each word on the basis of the score wheter it is positive, negative and neutral.
We used Naive Baye's Classifier as our base.
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...csandit
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to high-profile news. Such volatility, can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information.
Such mechanisms include statistical data only, without considering the collective feeling. This article, is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem
A MODEL BASED ON SENTIMENTS ANALYSIS FOR STOCK EXCHANGE PREDICTION - CASE STU...cscpconf
Predicting the behavior of shares in the stock market is a complex problem, that involves variables not always known and can undergo various influences, from the collective emotion to the high-profile news. Such volatility can represent considerable financial losses for investors. In order to anticipate such changes in the market, it has been proposed various mechanisms to try to predict the behavior of an asset in the stock market, based on previously existing information. Such mechanisms include statistical data only, without considering the collective feeling. This article is going to use natural language processing algorithms (LPN) to determine the collective mood on assets and later with the help of the SVM algorithm to extract patterns in an
attempt to predict the active behavior. Nevertheless it is important to note that such approach is not intended to be the main factor in the decision making process, but rather an aid tool, which combined with other information, can provide higher accuracy for the solution of this problem.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
2. A Karahoca and D Karahoca
http://www.iaeme.com/IJMET/index.asp 916 editor@iaeme.com
in today's digital world, where thanks to the reach and penetration of the internet, opinions at a
global scale are available. That is all that it takes today to make a difference. The micro blogging
site called Twitter, has fast emerged as one of the most powerful social media sites which can
sway opinions with 140 characters.
Sentiment or opinion analysis has of late emerged one of the most researched and talked about
subject in Natural Language Processing (NLP), thanks mainly to sites like Twitter. In the past,
sentiment analysis models using Twitter data have been built to predict sales performance, rank
products and merchants, public opinion polls, predict election results, political standpoints,
predict box-office revenues for movies and even predict the stock market. There are a plethora of
start-up companies which have emerged who are very vociferously engaging in
sentiment/opinion analysis for maximizing their revenues. However, gathering opinion or
analyzing sentiment in not as straight forward as it seems. Like mentioned, above, it is one of the
most challenging problems in NLP.
With the recent surge in the availability of data, companies the world over, are leveraging the
power of gaining insights from data to solve real world problems or for achieving business goals.
The volume, velocity and variety of data being generated have reached unprecedented rates. Not
only has this called for newer platforms like Hadoop, to handle big data, but also new machine
learning techniques and algorithms to derive insights from the data. The focus of this project is
on one such technique to handle both structured and unstructured data. Sentiment Analysis
defined as "the use of natural language processing, text analysis and computational linguistics to
identify and extract subjective information in source materials." The basic idea behind Sentiment
Analysis is to extract an opinion. And opinions do matter. In today's highly connected world,
social networking sites rule the roost. The number of "likes", "dislikes", "retweets", ratings etc.
sway the core thinking of human beings. Today, product managers are more concerned about
opinions on social networking sites of their products, rather than feedback provided on their site.
Movie reviews and restaurant reviews on such social networking sites dictate the amount of profit
the movie producer or restaurant owner is likely to rake in. Sentiment Analysis essentially looks
at classifying the polarity of text, emoticons and now days, even images and videos. The aim is
to find out whether the source material is positive, negative or neutral. A lot of the sentiments
expressed through social networking sites may not get captured by more traditional survey
questions. Sentiment Analysis bridges this gap.
2. BACKGROUND
A comparative analysis on techniques used for sentiment analysis shows techniques utilizing both
lexicon and non-lexicon based approaches for polarity identification. The paper also reflects on
a multilingual approach and concludes by stating that no existing technique is language
independent, thereby prompting a case for a generalized Sentiment Analyzer [1]. Xia and Li
(2015) claim about dual sentiment analysis. The study proposes a model to handle the polarity
shift problem and brings out the inadequacies of the bag of words (BOW) approach. The proposed
model creates reversed reviews that are sentiment-opposite to the original reviews, and make use
of the original and reversed reviews in pairs to train a sentiment classifier and make predictions
[2]. Similar to the organization of Gmail's inbox, Shetty et. al (2014), propose classification of
Facebook news feeds based on sentiment analysis. The proposed model automatically identifies
“important” feeds which reduce manual survey work which is done for drawing conclusions on
opinions posted on Facebook [3]. Islam (2014) describes a procedure of obtaining a unified user
rating system for Google Play Store apps by sentiment analysis on written reviews as well as the
starred ratings [4]. The use of sentiment analysis of social media content for forecasting election
results (Pakistan general elections 2013) has been shown in Razzaq 2014. The results obtained
have shown remarkable accuracy to the actual outcome of the elections [5].
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Another unique use of Twitter data has been to gauge users' response to popular smart phone
brands and their underlying operating systems. The results show that although the Twitter data
does provide some information about users' sentiments to the popular smart phone brands and
their underlying operating systems, the amount of data available for different brands varies
significantly. This limitation makes the comprehensive analysis of users' response somewhat
more challenging for some brands compared to others and consequently makes the comparison
between brands almost impossible [6]. Multi-dimensional sentiment analysis on restaurant ratings
has been undertaken to add special contexts and pricing as two other major aspects in the AAA
Restaurant Diamond Rating Guidelines to rate a restaurant. The data for this study was scraped
from http://Yelp.com. Results from fitting a multilevel model showed that the sentiments about
each of these five aspects alone explained about 28% of the explainable between-restaurant
variances, and 12% of the explainable within-restaurant variances of the restaurants' star ratings
[7].
The ability to identify opinions in the presence of diverse modalities is becoming increasingly
important. This study experiments with several linguistic, audio, and visual features, and shows
that the joint use of these three modalities significantly improves the classification accuracy as
compared to using one modality at a time [8].
Similarly Wollmer et al., focuses on automatically analyzing a speaker's sentiment in online
YouTube videos containing movie reviews. In addition to textual information, this approach
considers adding audio features typically used in speech-based emotion recognition, as well as
video features encoding valuable valence information conveyed by the speaker [9].
The attention on Twitter for sentiment analysis is immense. This study reports on the design
of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this
development. Results classify customers' perspective via tweets into positive and negative, which
is represented in a pie chart and html page [10]. Bhuta et al. (2014) reviews a number of
techniques, both lexicon-based approaches as well as learning based methods that can be used
for sentiment analysis of Twitter text [11].
Use of an SVM classifier combined with a cluster ensemble offers better classification
accuracies than a stand-alone SVM. The study proposes an algorithm which can refine tweet
classifications from additional information provided by clusters, assuming that similar instances
from the same clusters are more likely to share the same class label [12].
Singh et al. (2013) present an experimental study on a new kind of domain specific feature-
based heuristic for aspect-level sentiment analysis of movie reviews. The methodology adopted
analyses the textual reviews of a movie and assigns it a sentiment label on each aspect. The scores
on each aspect from multiple reviews are then aggregated and a net sentiment profile of the movie
is generated on all parameters [13].
Detection of anomalies in tweets in a timely manner can be very beneficial. In this study, the
authors survey existing anomaly analysis as well as sentiment analysis methods and analyses their
limitations and challenges. To tackle the challenges, an enhanced sentiment classification method
is proposed and discussed [14].
Beltagy and Ali (2013) highlight the major problems and open research issues that face
sentiment analysis of Arabic social media. The paper also presents a case study the goal of which
is to investigate the possibility of determining the semantic orientation of Arabic Egyptian tweets
and comments given limited Arabic resources. One of the outcomes of the presented study is an
Egyptian dialect sentiment lexicon [15].
Duwairi (2015) investigates sentiment analysis in Arabic tweets with the presence of
dialectical words. The study uses machine learning techniques to determine the polarity of tweets
written in Arabic with the presence of dialects. Dialectical Arabic is abundantly present in social
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media and micro blogging channels. Dialectical Arabic presents challenges for topical
classifications and for sentiment analysis [16].
For the Turkish language, Demirci (2014) has carried out an emotion analysis on Turkish
Tweets. Rather than doing a sentiment analysis, the author talks about emotion analysis. He
classifies emotions as joy, sadness, anger, fear, disgust and surprise. He also brings out that the
Turkish language is an agglutinative language. This creates problems as far as analysis is
concerned because each derivational suffix has the possibility of changing the meaning of the
word, and hence to obtain the real meaning of a word each derivational suffix must be examined.
Using Twitter is beneficial because of the sheer volume of the data generated. The author has
chosen Zemberek (https://code.google.com/p/zemberek/) as a multiple morphological analyser
for the study. Zemberek is an open source, platform independent, general purpose Natural
Language Processing library and toolset designed for Turkic languages, especially Turkish. In
the study, the author has utilized several types of classifiers. Bayesian classifier is selected since
it gives the probability of the instance to be in a class. SVM is selected to utilize both linear and
non-linear models. k-NN, which uses similarities of the instances, is chosen as an inherently
multi-class classifier. Tools used in the study are Zemberek, LIBSVM and WEKA.
For the purpose of data collection, the author has compiled a list of words and phrases for
each emotion class. Keyword based search is applied to construct a data set with ready to use
class labels. In the pre-processing phase, all non-emotional content is removed. This includes
removal of hashtags, URLs, links and user names. Thereafter, these are subjected to
morphological analysis to correct mistyped words. For feature vector construction, different
combinations of ngrams, part of speech tagging, emoticons and punctuation marks is tried out
with term count and tf-idf statistics. Feature selection is implemented through information gain
with term count and term frequency-inverse document frequency as the weighing factors. As
mentioned earlier, WEKA, LIBSVM and Matlab are the tools used for the classifiers - Naive
Bayes, Compliment Naive Bayes and k-nearest neighbours. The study also uses pronouns and
textual numbers as stop words [17].
Though the work done in this study is indeed creditable, it lacks the capability of handling a
wide range of very diverse tweets primarily because of the absence of a comprehensive keyword
list. A data set containing more diverse and a greater number of tweets may better generalize the
tweets in Turkish. Also the work does not cater for neutral tweets. Elimination of such non-
emotional tweets will help in better classification.
3. DATA AND METHODS
This research is examining the ability of different machine learning classifiers in classifying
sentiment in twitter feeds. Five common machine learning methods for supervised classification
of data include support vector machines, random forests, boosting, maximum entropy, and
artificial neural networks. These methods have demonstrated ability to classify data including
text data with desirable performance outputs across a wide range of problem types [18].
3.1. Data Gathering and Pre-processing
Twitter feeds from hepsiburada.com and pegasus.com were obtained. The 717 tweets from
hepsiburada.com and 129 tweets from pegasus.com (846 total tweets) consisted of opinions about
travel. The language of the twitter feeds was Turkish. In addition, a lexicon of 3579 words was
generated with sentiment ranging from very negative (-5) to very positive (+5), including a
variety of sentiments in-between. The lexicon ratings were performed by the author. Of these
words in the lexicon, 2,821 had a score of zero, not having any positive nor negative sentiment.
The twitter feeds were then pre-processed by using R Statistical Software (version 3.2.3) with
packages for text-mining (tm, version 0.6-2) and text classification (RTextTools, version
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1.4.2). A document-term matrix was created, excluding words with less than two letters,
removing numbers, removing punctuation, and converting punctuation to lower case. The matrix
consisted of 3,732 words (listed as columns) across the 846 tweets (listed as rows) with number
of occurrences in the tweet as values. Most words were represented in a tweet just once with 138
tweets included a word twice and 3 tweets included a word three times. An example of a
document-term matrix based on three tweets is described in Table 1. The tweets are “mobil
alisveris yukseliste...” (Document 1), “umarim yarin teslimatim yapilir..” (Document 2), and
“resmen kandirildim cok yazik” (document 3). Each tweet is considered a “document” and are
represented by rows. Each word is considered a “term” and represented in the column with each
number representing a word ordered alphabetically (e.g., “alisveris” = 1, “cok” = 2, “kandirildim”
= 3, “mobil” = 4, etc.). The cells represent the number of times a term (i.e., a word) is mentioned
in the document (i.e., a tweet). The document term matrix is a sparse matrix, with many cells
containing no information. If not empty, most of the cells in the table are represented by the value
of 1, which is the number of times that a term is represented in a document as similar in.
Table 1 Sample document term matrix
Document 1 2 3 4 5 6 7 8 9 10 11
1 1 1 1
2 1 1 1 1
3 1 1 1 1
3.2. Scoring of Sentiment of Tweets
For each tweet, a total sentiment score was calculated based upon the matrix and the lexicon. The
total sentiment score for a tweet was equal to the sum of the sentiment score of each word times
the number of the times the word was represented in eq. 1.
= ∑ (1)
Where Stweet denotes the total sentiment score for a tweet, sn is the sentiment score of a
word n and wn is the number of times word n appears in a tweet. Equation 1 could be applied to
Table 1. As mentioned previously, most words had a sentiment score of 0. For the tweets used to
create Table 1, “umarim(hope so)” (Term 7) had a score of +1, “cok(much)” (Term 2) had a score
of +1, and “kandirildim(deceived)” (Term 3) had a score of -4. The total sentiment score for
document 1 would be 0 (0*1 + 0*1 + 0*1), for document 2 would be 1 (1*1 + 0*1 + 0*1 + 0*1),
and for document 3 would be -3 (1*1 + -4*1 + 0*1 + 0*1). From the Table 1 example, documents
1, 2, and 3 would have raw scores of 0, 1, and -3, respectively. Under the scoring system from
Equation 1, the sentiment scores for a tweet ranged from -11 to +11 (Table 2). The raw scores
were further processed to develop two alternative scores. A tweet score was converted to a simple
positive/negative scoring system (-1, 0, +1) based on the total positive, neutral, and negative
scores. Tweets with a total score of -10 or -3 were both considered negative tweets and the
magnitude of the negative or positive score was ignored. Also, a tweet score was converted to a
scaled score (-2, -1, 0, +1, +2) whereby scores of -11 to -5 were reassigned a score of -2, scores
of -4 to -1 were reassigned a score of -1, scores of 1 to 4 were reassigned as score of 1, and scores
of 5 to 11 were reassigned a score of 2. This scoring differentiated the very negative or very
positive scores. The simple positive/negative scoring system for documents 1, 2, and 3 would be
0, 1, and -1, respectively. The scaled scores for documents 1, 2, and 3 would also be 0, 1, and -1,
respectively. These two processed scores (positive/negative and scaled) were used as the response
variable for the supervised classification step. The 846 tweets were reshuffled to create a random
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ordering of tweets for further analysis. The tweets were separated into a class for training the
models (700) and for testing (146).
Table 2 Distribution of sentiment scores for tweets
Score
Number of
Tweets
Score Number of Tweets
-11 1 1 107
-9 2 2 64
-8 5 3 31
-7 11 4 21
-6 9 5 17
-5 39 6 7
-4 34 7 1
-3 55 8 2
-2 72 9 1
-1 136 10 1
0 229 11 1
The model training and testing were performed using the RTextTools package [19]. Within
the RTextTools package, classification algorithms used for the analysis included support vector
machines (SVM), random forests, boosting, maximum entropy (MAXENT), and artificial neural
networks (ANN). Models were generated using the 700 tweets for model training and the
predictions based on the 146 tweets for testing were compared with the actual sentiment values.
For all final model outputs the proportion accuracy (eq. 2), precision (eq. 3), and recall (eq. 4)
were calculated.
ACCURACY = (2)
PRECISION = % &
(3)
RECALL =
% &
(4)
SPECIFICITY = % &
(5)
In addition, receiver operating characteristic (ROC) curves were determined u using the
pROC package (version 1.8) within R Statistical Software. Within the ROC curve, the x-axis
values represent (1 – specificity (eq. 5)). The y-axis values represent sensitivity, which is the
same as recall (eq. 4).
4. FINDINGS
4.1. Twitter Data
Overall, the twitter data had a higher degree of negativity. The training data had tweets that were
43% negative, 26% neutral, and 31% positive. On a five-category scaled breakdown, the tweets
were 9% very negative, 32% negative, 33% neutral, 23% positive, and 3% very positive. The test
data had tweets that were 41% negative, 33% neutral, and 26% positive. On a five-category scaled
breakdown, the tweets were 8% very negative, 36% negative, 26% neutral, 27% positive, and 4%
very positive.
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4.2. Accuracy Assessment
The examination of sentiment using five different algorithms resulted in very similar results for
the analysis of positive/negative sentiment scores and for the scaled sentiment scores. The
accuracy based on the training data set was higher than the accuracy determined from the test
data set for both the positive/negative sentiment scores and the scaled sentiment scores. With
support vector machines, random forests, maximum entropy, and artificial neural networks, the
difference between the training and test data accuracy was over 0.4, whereas with boosting, the
difference was no greater than 0.25. For the accuracy assessment of the positive/negative
sentiment scores in Figure 1, the values ranged from 0.49 to 0.54. The precision and recall values
were similar. The most accurate classification algorithm was the random forest algorithm at 54%.
The least accurate classification algorithm was the boosting algorithm.
Figure 1 Accuracy assessment of the positive/negative sentiment scores
For the accuracy assessment of the scaled sentiment scores (Fig. 2), the values ranged from
0.38 to 0.40. The precision and recall values were similar. The classification algorithm for the
scaled sentiment scores had lower accuracy values (10-15% less) compared to the
positive/negative sentiment score classification. The most accurate classification algorithms were
support vector machine, random forest, and the maximum entropy algorithms. The least accurate
classification algorithm was the boosting algorithm.
Figure 2 Accuracy assessments of the scaled sentiment scores
4.3. Receiver Operating Characteristic Curve
Figure 3 shows the ROC curves for each of the five classification algorithms for the
positive/negative scores and for the scaled scores. The area under the curve for the
positive/negative scores ranged from 0.545 to 0.644. The boosting algorithm provided the lowest
AUC and the maximum entropy provided the highest AUC. The area under the curve for the
0.83
0.97
0.63
1 1
0.91
0.98
0.74
1 1
0.75
0.95
0.63
1 1
0.4 0.4 0.38 0.4 0.4
0.26 0.28 0.32 0.31 0.250.26 0.26
0.29 0.3 0.27
0
0.2
0.4
0.6
0.8
1
1.2
Support Vector
Machine
Random Forest Boosting Maximum Entropy Neural Network
Accuracy (Training) Precision (Training) Recall (Training) Accuracy (Test) Precision (Test) Recall (Test)
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scaled scores ranged from 0.531 to 0.588. The random forest algorithm provided the lowest AUC
and the maximum entropy provided the highest AUC.
Figure 3 Receiver operating curves for classification of twitter sentiments using support vector
machines, random forests, boosting, maximum entropy, and artificial neural networks for a)
positive/negative scores and b) scaled scores
5. DISCUSSION AND CONCLUSION
The examination of accuracy from training and test data indicated the algorithms had a large
degree of overfitting. This was especially true for support vector machines, random forests,
maximum entropy, and artificial neural networks. Boosting had a lesser degree of overfitting.
These results reinforce the need to have accuracy assessments on separate data from the data used
to develop the model in order to avoid over-optimistic assessments of accuracy.
The level of accuracy declined with the increase in number of classes. The positive/negative
score classification involved three classes (positive, neutral, and negative) and the scaled score
classification involve give classes (very positive, positive, neutral, negative, and very negative).
As more classes are added, the spatial extent of the classes was reduced, resulting in a smaller
classification target footprint. The consequence would be greater likelihood of misclassifications.
The percent accuracy for all algorithms were similar and around 0.5-0.55 for the
positive/negative scores and the 0.4 for the scaled scores. These accuracy scores were similar to
what was observed before for other studies classifying text using machine learning algorithms.
Previously, Hsu et al. (2010) recorded 47% accuracy for a five-category sentiment analysis
project using SVMs. Similarly, Socher (2014) recorded 49.7% accuracy for the same five-
category sentiment analysis. Also, Go et al. (2009) recorded 80.5% and 82.2% accuracy for
classifying positive and negative (no neutral) sentiments using maximum entropy and SVMs,
respectively. In addition, Pang et al. recorded accuracies of 72.8% to 82.9% using support vector
machines on positive/negative sentiments (no neutral sentiments).
The main distinction between the higher accuracy measurements in past sentiment analyses
with machine learning algorithms with the current effort is the number of classes. The studies
recording accuracies in the 80% range dealt with only positive and negative comments (two
classes) and did not consider neutral comments. The current effort include neutral comments.
Thus, it was understandable why the accuracies were not as great with the current effort. The
other studies that looked at five classes also reported less accurate assessments.
In light of the results from other studies and in consideration of the differences, the accuracy
of the classifications from the current effort were within reason. With three classes, about 50-
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55% accuracy would be appropriate. After factoring in the advanced work performed to fine-tune
the parameters, the level of accuracy for classification of sentiment using this dataset was high
highest level expected.
Another phenomenon was the convergence in performance among the five algorithms. The
differences among algorithms were relatively small. Other comparative studies have shown that
different machine learning algorithms have had markedly different results [18-21]. The current
effort did show differences in performance, but the differences suggest similar performance.
There are natural questions on whether the five algorithms used were the best algorithms. These
five represent the highest performing machine learning algorithms [18]. It is unlikely that another
algorithm would have drastically greater level of performance for the given data set. The
algorithms used within this study were controlled through commands within the RTextTools
package. The commands within RTextTools facilitated pre-processing of the twitter feeds,
removing two-letter words, punctuation, and numbers. The commands within RTextTools called
upon machine learning algorithms in existing R packages, facilitating the use of multiple machine
learning packages within a systematic approach. However, within R Statistical Software,
alternate packages not called upon by RTextTools may result in different performances. For
artificial neural networks, the neuralnet package may offer some improvements. For boosting,
the gbm package may offer greater fine-tuning features and improvements. For random forests,
the conditional inference forests of the party package may offer improvements over the known
biases of the randomForest package.
A methodological issue may have been the scoring of the lexicon. The lexicon sentiment
scores were assigned by one individual. Without having an established lexicon with associated
emotion and sentiment values, the assignment of sentiment scores by just one individual was
needed. Due to this methodological limitation, there may have been a bias. If the lexicon was
developed by a larger group of people or have been peer-reviewed, the scoring may have been
different. This may have resulted in a more clearly defined, consistent scoring system that would
have led to clearer classifications.
The current work classified twitters sentiments with estimated accuracies of up to 55% for
three-class outputs using five machine learning algorithms including support vector machines,
random forests, boosting, maximum entropy, and artificial neural networks. The outputs were
similar across the different algorithms. The current work advances sentiment analysis work using
a comparative evaluative approach. Future work would involve ways to improve the outputs. The
current work involved five machine learning algorithms. Whether these are the best algorithms
needs further consideration in light of alternative versions of a particular algorithm. Future work
could also use additional machine learning algorithms. In light of the comprehensive approach
undertaken with the current work, which includes a diversity of algorithms and thorough efforts
to fine-tune the algorithms, drastic improvements may be unlikely. Future work could also focus
on improving the lexicon and the scoring within the lexicon. The sentiment scores assigned to
the lexicon used for this study were not meant to be the final authority. Future improvements on
scoring with more individuals would facilitate creation of a more robust lexicon and scoring
system.
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