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.
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.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
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Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
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.
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Cloud Technologies providing Complete Solution for all
AcademicProjects Final Year/Semester Student Projects
For More Details,
Contact:
Mobile:- +91 8121953811,
whatsapp:- +91 8522991105,
Office:- 040-66411811
Email ID: cloudtechnologiesprojects@gmail.com
Sentiment analysis in twitter using python
Sentiment analysis is the interpretation and classification of emotions (positive, negative and neutral) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations and feedback
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.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi Perspective Matching for Natural Language Sentence BiMPM , which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection. Prasanth. K | Praveen. N | Vijay. S | Auxilia Osvin Nancy. V ""Fake News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30014.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/30014/fake-news-detection-using-machine-learning/prasanth-k
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Sentiment analysis or opinion mining is a process of categorizing and identifying the sentiment expressed in a particular text. The need of automatic sentiment retrieval of
the text is quite high as a number of reviews obtained from the Internet sources like Twitter are huge in number. These reviews or opinions on popular products or events help in determining the public opinion towards the issue. An averaged histogram model is proposed in the process that deals with text classification in continuous variable approach. After data cleaning and feature extraction from the reviews, average histograms are constructed for every class, containing a generalized feature representation in that particular class, namely positive and negative. Histograms of every test elements are then classified using k-NN, Bayesian Classifier and LSTM network. This work is then implemented in Android integrated with Twitter. The user will have to provide the topic for analysis. The Application will show the result as the percentage of positive review tweets in favor of the topic using Bayesian Classifier.
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.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
Recently, fake news has been incurring many problems to our society. As a result, many researchers have been working on identifying fake news. Most of the fake news detection systems utilize the linguistic feature of the news. However, they have difficulty in sensing highly ambiguous fake news which can be detected only after identifying meaning and latest related information. In this paper, to resolve this problem, we shall present a new Korean fake news detection system using fact DB which is built and updated by human's direct judgement after collecting obvious facts. Our system receives a proposition, and search the semantically related articles from Fact DB in order to verify whether the given proposition is true or not by comparing the proposition with the related articles in fact DB. To achieve this, we utilize a deep learning model, Bidirectional Multi Perspective Matching for Natural Language Sentence BiMPM , which has demonstrated a good performance for the sentence matching task. However, BiMPM has some limitations in that the longer the length of the input sentence is, the lower its performance is, and it has difficulty in making an accurate judgement when an unlearned word or relation between words appear. In order to overcome the limitations, we shall propose a new matching technique which exploits article abstraction as well as entity matching set in addition to BiMPM. In our experiment, we shall show that our system improves the whole performance for fake news detection. Prasanth. K | Praveen. N | Vijay. S | Auxilia Osvin Nancy. V ""Fake News Detection using Machine Learning"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-2 , February 2020,
URL: https://www.ijtsrd.com/papers/ijtsrd30014.pdf
Paper Url : https://www.ijtsrd.com/engineering/information-technology/30014/fake-news-detection-using-machine-learning/prasanth-k
This documentation provides a brief insight of face recognition based attendance system using neural networks in terms of product architecture which can be used for educational purpose.
Sentiment Analysis/Opinion Mining of Twitter Data on Unigram/Bigram/Unigram+Bigram Model using:
1. Machine Learning
2. Lexical Scores
3. Emoticon Scores
YouTube Video: https://youtu.be/VuR16P87yPE
Link to the WebPage: http://akirato.github.io/Twitter-Sentiment-Analysis-Tool
Github Page: https://github.com/Akirato/Twitter-Sentiment-Analysis-Tool
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
It gives an overview of Sentiment Analysis, Natural Language Processing, Phases of Sentiment Analysis using NLP, brief idea of Machine Learning, Textblob API and related topics.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Sentiment analysis or opinion mining is a process of categorizing and identifying the sentiment expressed in a particular text. The need of automatic sentiment retrieval of
the text is quite high as a number of reviews obtained from the Internet sources like Twitter are huge in number. These reviews or opinions on popular products or events help in determining the public opinion towards the issue. An averaged histogram model is proposed in the process that deals with text classification in continuous variable approach. After data cleaning and feature extraction from the reviews, average histograms are constructed for every class, containing a generalized feature representation in that particular class, namely positive and negative. Histograms of every test elements are then classified using k-NN, Bayesian Classifier and LSTM network. This work is then implemented in Android integrated with Twitter. The user will have to provide the topic for analysis. The Application will show the result as the percentage of positive review tweets in favor of the topic using Bayesian Classifier.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This paper is explaining that how only leadership is responsible for sustainable improvement and
growth and only it can lead the organization towards improvement and overall development. Leadership and its
effectiveness are discussed in this research work and also how leadership is a different way of the success of the
organization and different from the traditional management to create true work-culture and good-will of the
organization in the social scene. Leadership is only responsible in bringing positive and negative change in the
organization; if the leadership doesn’t have the concern in the organization, the organization will not be able to
lead in the right direction towards improvement and development.
The topic of assignment is a critical problem in mathematics and is further explored in the real
physical world. We try to implement a replacement method during this paper to solve assignment problems with
algorithm and solution steps. By using new method and computing by existing two methods, we analyse a
numerical example, also we compare the optimal solutions between this new method and two current methods. A
standardized technique, simple to use to solve assignment problems, may be the proposed method
Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
n today’s society, we stand before a change in energy scarcity. As our civilization grows, many
countries in thedeveloping world seek to have the standard of living that has been exclusive to a few nations, so
their arises a need in thedevelopment of technology that is compatible enough with the resources provided by
nature in order to have sustainabledevelopment to all class of the society. In order to overcomethe prevailing
challenges of huge energy crises in near future, there is an urgent need for the development of electrical
vehiclesor hybrid electrical vehicles with low CO2 emissions using renewable energy sources. In view of the
above, electrochemicalcapacitors can fulfil the requirements to some extent.Preparation of nano composite
polymer gel electrolyte is the best optional product to overcome these problems. When fillers are added or
dispersed to the polymer gel electrolyte, amorphous or porous nature of electrolyte increases which enhances
the liquid absorbing quality of polymer and helps in removing the drawbacks of polymer gel electrolytes such as
leakage, poor mechanical and thermal stability etc. In this work dispersion of SiO2 nano filler is done in the
[PVdF (HFP)-PC-Mg (ClO4)2] for the synthesis of nano composite PGE [PVdF (HFP)-PC-MgClO4- SiO2].
Optimization and characterization was carried out by using various techniques.
Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
view for new research in gas sensing application. In this paper we review various two dimensional sheet such as
Graphene, Boron Nitride nanosheet, Mxene and their application in sensing various gases present in the
atmosphere.
METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
most common reason for adulteration is the use of undeclared materials by manufacturers that are cheaper than
the correct and declared ones. The adulterants can be harmful or reduce the effectiveness of the product, or
they can be harmless.
The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
idea from core requires a systematic plan, time management, time investment and most importantly client
attention. The Time required for developing may vary from idea to idea and strength of the team. Leadership to
build a team and manage the same throughout the peak of development is the main quality. Innovations and
Techniques to qualify the huddles is another aspect of Business Development and client Retention.
Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
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.
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/
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Student information management system project report ii.pdf
Methods for Sentiment Analysis: A Literature Study
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Methods for Sentiment Analysis: A Literature Study
Shiv Dhar1
, Suyog Pednekar1
, Kishan Borad1
, Ashwini Save2
1
(B.E. Computer Engineering, VIVA Institute of Technology, University of Mumbai, India)
2
(Head of Department, Computer Engineering, VIVA Institute of Technology, University of Mumbai, India)
Abstract : 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.
Keywords– Machine Learning, Sentiment Analysis, CNN, analysis, AI, SVM, NLP.
1. INTRODUCTION
Sentiment analysis intents to define the attitude of a speaker, writer, or other subject with respect to some
topic or the overall contextual division or emotional response to a document, interaction, or event. It refers to the
use of natural language processing, text analysis, computational semantics, and biometrics to systematically
identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is broadly
applied to “voice of the customer” materials such as reviews and survey responses, as well as to online and
social media. Sentiment analysis has claims in a variety of domains, ranging from marketing to customer service
to clinical medicine.
Sentiment analysis stands at the intersection of natural language processing and large-scale data mining.
Sentiment analysis has important applications in academia as well as commerce. The understanding of human
language is a core problem in AI research. At the same time, with increasingly lowering barriers to the Internet,
it is easier than ever for end-users to provide feedback on the products and services they use. This information is
highly valuable to commercial organizations; however, the volume of such reviews is growing rapidly,
necessitating an automated approach to extracting meaning from the huge volume of data. This automated
approach is provided by sentiment analysis.
In this paper, various approaches to sentiment analysis have been examined and analysed. Techniques such
as lexicon based approach, SVM [4], Convolution neural network [9], morphological sentence pattern model
and IML algorithm are discussed. These techniques all have different strengths and weaknesses, and are have
different use cases. Their advantages and disadvantages are explored and compared.
2. SENTIMENT ANALYSIS TECHNIQUES
2.1 Sentiment Analysis on Twitter using Streaming API [8]
The propose system focuses on analyzing what people thinks about various products and services.
Many users share their opinions about various products and contents. Sentiment analysis helps in classifying the
positive or negative data. In the proposed system, it uses Natural language processing - NLTK, where it helps in
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tokenization, stemming, classification, tagging, parsing and sentiment reasoning. Its basic feature is to convert
unstructured data into structured data.
It uses Naive Bayes for classification which requires number of linear parameters. The system uses
Hadoop for extracting information and uses Twitter Application Programming Interface. The system gathers
streaming tweets using Twitter API and assigns every tweet a positive or negative probability.
The future system mainly focuses on real time sentiment analysis such as evaluating tweets from twitter. It
performs sentiment analysis, feature based classification and opinion summarization.
Advantages:
Classification is done in real time which makes it very efficient.
Sentiment analysis in this system uses Hadoop to load live data.
Several systems use stored tweets for classification, leading to high requirement of space, whereas here the
storage required is less.
Disadvantages:
While classifying, the words are accepted individually rather than in a fix pattern or complete sentence.
The semantic meaning is neglected as single words are scanned.
2.2 Neural Networks for sentiment analysis on twitter [7]
The proposed system mainly focuses on providing polar views by dividing the opinions in social media
and websites having customer reviews. It divides the reviews from websites and divide them into positive,
negative and neutral reviews. The system used feed forward neural network. The neural network used is on
MATLAB, using neural network toolbox. It reduces the input by removing the punctuations, single characters,
stop words like and, to, the etc. and also mentions to other users using @ symbol.
The system uses Porter’s stemming algorithm for stemming. Each tweet obtained is assigned a value
and arranged linearly in 2D table. It uses pattern matching in neural networks for checking the data.
The proposed system performs sentiment analysis on twitter using neural networks. Sentiment analysis is
performed by various methods, here it uses neural networks which helps in achieving more accuracy and
efficiency. Preprocessing is also implemented by the proposed system which helps in obtaining better results.
Advantages:
Tweets are easily classified into positives and negatives.
Preprocessing helps in improving the time required.
Reducing redundant data helps in gaining better accuracy.
Disadvantages:
The input is still comparatively large and thus require more time.
The input on twitter has #, which are connected having no space. This requires dividends and thus needs to be
implemented in future.’
2.3 Product related information sentiment-content analysis based on Convolution Neural
Networks for the Chinese micro-blog [9]
Sentiment analysis is performed by the suggested system on various Chinese micro-blogs. It performs
sentiment analysis to determine whether positive/negative or it is an advertisement. The system uses convolution
neural networks for classification. And support vector machine algorithm (SVM algorithm) is used. It reduces
the size of input data by breaking down major data set containing all the information into smaller data set by
removing unwanted data like author's name, duplicated data and similar texts. It uses CNN, which has four
layers namely input layer, convolution layer, pooling layer and fully connected layer. SVM and lexicon analysis
is used as baseline.
In the proposed system sentiment analysis of Chinese micro-blogs is performed using CNN. There are
many product advertisements and promotions in micro-blogs which can be detected using this process. The
system is quite useful in removing the redundant data such as advertising and promotions, resulting in better
results for sentiment analysis.
Advantages:
It provides better result than earlier lexicon analysis.
Along with positive and negative statement, it also determines advertisement.
It also provides better result than SVM.
Disadvantages:
It takes an entire sentence or concatenated sentence as input. Thus, more time is required for analysis.
Here entire data results in more time required to analyze than just predefined patterns.
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It uses sentence embedding viz. less effective than character embedding.
2.4 Convolution Neural Networks based sentiment analysis using Adaboost combination [6]
The proposed system focuses on feedback analysis. This paper states various techniques for sentiment
analysis classification. It includes SVM, Naive Bayes, recursive neural network, auto encoders. It also states
various methods for identifying the different roles of specific N-grams. It uses Adaboost to combine different
classifiers.
The system proposes preprocessing using sentence matrix input, i.e. the input matrix in classified and
used as input in a matrix. Here N-grams are formed which are used for dividing into smaller segments according
to the N. It uses Adaboost algorithm to combine week classifiers with the strong classifiers. Parameter
overfitting is checked and reduced using regularization. It drops certain parameters which are not defined
earlier. The data set used in this system are Movie Review and IMDB.
Advantages:
It uses boosted CNN to provide better results than general CNN.
The proposed model separates the features by passing the documents and then boost the classifiers trained on
these representations.
Disadvantages:
Although it uses N-gram approach, it still must cover the total input and thus time required is high.
There can be more layers added in CNN for better result.
2.5 A Feature based approach for sentiment analysis using SVM and Co-Reference Resolution
[4]
Online shopping is trending these days as it’s found secure. People buy products online and post their
reviews on it. These are in the form of tweets or product reviews. It is difficult to manually read these reviews
and assign sentiment to them. So, for all these tweets an automated system can be created which will analysis
the review and extract the user percepts. In this paper they have developed a producer for feature based
sentiment analysis by a classifier called Support Vector Machine.
In this paper they have used machine learning approach called supervised classification which is more
accurate than all other methods as the classifier is to be trained using the real-world data set. They used
SentiWordNet which is created mainly for opinion mining. As every word in SentiWordNet have 3 polarities as
Positive, Negative and Subjective. SVM is used because sentiment analysis is a binary classification and it has
capability to work on huge datasets. Co-Reference Resolution is to remove the repetitions of words in a sentence
and to develop the relation between two sentences for the sentiment analysis.
Advantages:
Combination of SVM and Co-Reference Resolution improves the accuracy of feature based sentiment analysis.
SentiWordNet helps to find the Polarities of the opinion words.
Disadvantages:
Sarcastic reviews are different to identify for computer as well as human.
Some reviewers may spam the reviews which is different to identify.
Reviewers may post advertisement which is to be detected and discarded.
2.6 SentiReviews: Sentiment analysis based on Text and Emoticons [5]
Sentiment using emoticons is increasing gradually on social networking. People comment, tweets post
their opinions using test as well as emoticons. Which increase the difficulties in analysis the sentiment of the
reviewer. Various factors that affect sentiment analysis are discussed here but the focus is on the emoticons and
the role of emoticons in sentiment analysis also various issues like sarcasm detection, multilingualism handling
acronyms and slang language, lexical variation and dynamic dictionary handling are discussed. Users these days
use emoticons to express most of their emoticons, text communication erase the uses of emoticons.
Sentiment analysis can be done based on two approaches, Lexicon based approach and Machine Learning
approach. The Lexicon is the vocabulary of person, language or branch of knowledge used for calculating
polarities of sentiment, in the Lexicon based approach. In Machine Learning approach, approach the machine/
computer learns the sentiment on regular bases and the polarities are assign.
Advantages:
Various methods are available to find the sentiment in a tweet or review.
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Various approaches can be used to detect the sentiment from the feature based sentiment analysis.
Disadvantages:
Emoticons in the sentences, tweets or review is the problem to define the sentiment.
Dealing with the variation of lexicon can be challenging task in sentiment.
2.7 A feature based approach by using Support Vector Machine for sentiment analysis [10]
As the modern era of globalization, e-commerce is growing in vast number so as their opinion also, but
it is very difficult to identifying whether it is positive, negative or neutral and it would be tiresome job to study
all the opinion manually. To find out the sentiment an automated system must be developed “Support Vector
Machine” can be used for this method. SVM is machine that takes the input and store them in a vector then
using SentiWordNet it scores it decides the sentiment. It also classifies the opinion in overall way by positive,
negative or Neutral.
Advantages:
The accuracy rate is increased as each word in the opinion are scored and the overall sentiment is given.
It can work on large data a single time.
Disadvantages:
Sarcasm detection can be a problem.
Anaphora Resolution is most user ignores the pronouns.
Grammatical mistakes of user.
2.8 Localized twitter opinion mining using sentiment analysis [11]
As the public information from social media can get interesting result and the public opinion on any
product. Service or personally is most effective and it is necessary to find this information from social media.
Sentiment analysis mining using Natural Language Processing, Rapid miner, SentiWord, SNLP as mining of all
the opinion on social media has become a necessity for the analysis of sentiment from the user. Stanford NLP is
used to extract the sentiment from an opinion, Rapid Miner is used to mine all the opinion, tweets from the
social using a keyword, SentiWord is used to assign the polarities to the keywords in the opinion.
Advantages:
Mining of opinion using keyword of product is done faster.
Polarities assignment helps to analysis the opinion.
Disadvantages:
Emoticons used in tweets can be difficult to result the sentiment.
Co-reference Resolution in opinion must be serious issue
2.9 A Method for Extracting Lexicon for Sentiment Analysis based on Morphological Sentence
Patterns [1]
Aspect-based sentiment analysis is higher-quality and more in-depth than, the probability-based model.
But building the aspect-expression pairs is a challenge (manually is slow, obviously). An unsupervised approach
to building aspect-expression pairs is proposed. The natural morphological (i.e. grammatical) patterns in
sentences are exploited to build aspect-expression pairs. It uses POS tagging to find expression candidates for
aspects. Thus, it builds aspect-expression pairs which are then analyzed for sentiment.
Advantages:
The biggest advantage is that aspect-based sentiment analysis can be done automatically, in an unsupervised
manner.
This helps us scale this in-depth approach to large datasets and new data, without human intervention.
It does so while maintaining or increasing classification accuracy.
Disadvantages:
The aspect-based approach is an all-or-nothing approach.
That is, if an aspect-expression pair is found, then results are usually quite accurate.
But if no aspect-expression pair is found, then that review or tweet cannot be processed further, rendering it
effectively into useless noise.
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2.10 Sentiment Analysis Using Machine Learning and Lexicon-based Approaches for Student
Feedback [2]
Evaluation of instructors and courses at the end of the semester is becoming a standard practice. Along
with scale-based feedback, students also provide textual feedback about the courses; this feedback can be
analyzed for sentiment.
The paper recommends a hybrid methodology to sentiment analysis using both machine learning and
lexicon-based approaches. This hybrid methodology yields an enhanced result than the lexicon-based approach
or machine-learning approaches alone.
System Used:
The process methodology is as follows:
1. Dataset Description:
The dataset was manually labelled as positive, negative, neutral.
Thus, it is a supervised dataset.
2. Preprocessing:
The Python NLTK package was used to perform preprocessing: punctuation, tokenization, case conversion, stop
words.
3. Data Partition:
The training test ratio of the dataset was 70:30.
TF-IDF (Term Frequency - Inverse Document Frequency).
The words that occur frequently in the dataset but not in a ‘neutral’ corpus are assigned a higher weight.
N-gram Features, Unigram (1-word) and bigram )2-word) features were extracted.
Lexicon Features, the semantic orientation was determined using an existing sentiment dictionary.
4. Model Training:
The hybrid model for sentiment analysis was trained using unigrams, bigrams, TF-IDF and lexicon-based
features.
To train the model, random forest and support vector machine (SVM) algorithms were used.
This paper yielded a marginally better result than purely lexicon-based approaches.
It outperforms many commercial implementations such as Microsoft’s API, Alchemy, and Aylien.
2.11 A Context-based Regularization Method for Short-Text Sentiment Analysis [3]
The authors suggest a fusion model that combines word-similarity knowledge and word-sentiment
knowledge. They use the contextual knowledge obtained from the data to improve classification accuracy.
System:
To compute the sentiment polarity of a word, TRSR (TextRank Sentiment Ratio) is used. Word-
embedding is used to compute the similarity between words.
This contextual knowledge obtained is not statistical but on the semantic level.
These two regularizations are combined as a classification model, which converts it to an optimization problem
which can be solved computationally.
The parameter obtained from training the model applies into the logistic regression, and we get the
final classification model. The hybrid model that combines word-similarity and word-sentiment performs better
than either of the approaches used individually.
2.12 Aspect-based Feature Extraction and Sentiment Classification using Incremental Machine
Learning Algorithm for Review Datasets [12]
The paper offers an approach for sentiment analysis using a planned iterative decision tree. Customer
reviews are collected and from them, sentiments and aspects are identified; this is called aspect-based feature
extraction. The authors compare the performance of their proposed system with baseline machine learning
algorithms like SVM and naive Bayes.
There are 3 stages in this system:
1. Data preprocessing.
Many preprocessing stages are used to remove irrelevant and noisy data.
2. Aspect-based sentiment analysis.
The aspects and expressions are identified. Sentiment analysis will be performed on these aspects.
3. Opinion summarization using iterative learning tree algorithm.
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It uses an iterative practice to categorize the given inputs for the assessment of sentiment. It starts comparison
from the root node and then compares it with every instance of data. Labels are assigned to the leaf nodes. Every
node in the tree represents an aspect.
Advantages:
This approach performs better than other algorithms like naive Bayes and SVM.
An incremental decision tree is much faster and better than a linear decision tree due to reduced memory and
limited buffer requirements.
Disadvantages:
Classification, while better, is nevertheless supervised, because he class labels need to be well-defined.
This means that it cannot be used for new, unstructured datasets.
3. ANALYSIS
Following table is a summary of studied research papers on Sentiment analysis and various techniques used.
Sr.
No.
Title Technique Used Dataset Accuracy
1 Sentiment analysis of student
feedback using machine
learning and lexicon based
approaches [2]
It uses a hybrid model.
It integrates TF/IDF +
lexicon with the machine
learning algorithms like
Random Forest and SVM.
1230 comments
from the institute's
education portal.
Accuracy:
0.93
F-measure:
0.92
Improved by
0.02
2 A context-based regularization
method for short-text
sentiment analysis [3]
It uses a classification model
that combines two
regularizations, word
similarity and word
sentiment.
Introduces new word-
sentiment calculating.
Movie comments
from Cornell Univ.
2016 US election
comments crawled
from Facebook.
S.C.D. from
Weibo.
Accuracy is
improved by
over 4.5%
baseline
3 A feature based approach for
sentiment analysis using SVM
and coreference resolution [4]
SVM for classification from
huge dataset.
Coreference resolution to
extract the relation from two
sentences.
Reviews from
ecommerce sites
Combining
coreference
and SVM, it
improves the
accuracy of
feature-
based
sentiment
analysis
4 SentiReview: Sentiment
analysis based on text and
emoticons [5]
Lexicon-based approach for
assigning polarities.
Machine learning approach
for constantly analyzing the
polarities.
Comparison for between
different methods to
analyses sentiment
polarities.
Twitter API
Weibo
Stating
various
methods
5 Convolutional Neural Network
based sentiment analysis using
Uses boosted CNN Model. Movie Review Accuracy is
89.4%
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adaboost combination [6] Adaboost algorithm is used
to regularize.
IMDB
Increased by
0.2%
6 Neural Network for Sentiment
analysis on Twitter [7]
Sentiment analysis using
feed forward neural
network.
Reducing input sequence by
removing &, @.
Tweets from
Michigan’s
sentiment analysis.
Twitter API
Accuracy
achieved is
74.15%.
7 Sentiment Analysis on Twitter
using streaming API [8]
NLTK for tokenization and
convert unstructured data to
structural data.
Uses Naive Bayes for
Classification.
Twitter API It performs
analysis on
real time
data.
8 Product based data sentiment
content analysis based on
Convolution Neural Network
for the Chinese micro-blog [9]
Sentiment analysis using
Convolution Neural
Network
Chinese Micro blog
database
Better
accuracy
than lexicon
analysis.
9 A feature based approach for
sentiment analysis by using
Support Vector Machine
(SVM) [10]
Support Vector Machine for
classification from huge
data.
Reviews of product
from e-commerce
site amazon, eBay
Accuracy
increased
total of
88.13%
10 Localized based Opinion
mining using Sentiment
Analysis [11]
Rapidminer is used for
mining which extract
information using keywords.
Sentiword is used for
assigning polarities.
Twitter API Various
processes for
extraction of
data.
11 Aspect based feature
extraction & sentiment
classification of reviews data
sets using Incremental
Machine Learning algorithm
[12]
Identifies the sentiment,
aspect & performs data
classification.
It uses incremental decision
tree for classification.
Opinion summarization
using SVM & Naive Bayes.
General Data The result
shows that
SVM
method is
much better
than Bayes.
12 Sentiment Analysis on Social
Media using Morphological
Sentences Pattern model [1]
Extracts aspects &
expression using sentiment
pattern analyzer based on
MSP model.
Movie reviews
from IMDb
Rotten Tomatoes
Twitter
YouTube
Accuracy
increased to
91%
Increased by
2.2%
8. Volume 1, Issue 1 (2018)
Article No. 8
PP 1-8
8
www.viva-technology.org/New/IJRI
4. CONCLUSION
In this suggested approach, extensive study of numerous methods and practices used for sentiment
analysis are considered. Methods such as lexicon based approach, SVM, SentiReview, CNN, IML and
Morphological Sentence Pattern model are studied in this paper. Each method holds its own unique ability and
provides different results. Lexicon based approach and SVM are the methods used in the past, but with the
advancement in sentiment analysis various methods such as CNN and IML are being practiced more for better
result. Sentiment analysis plays a vital part in reviewing any product, system, etc. The methods stated in paper
have their advantages and disadvantages and can be used according to the system.
Acknowledgement
We would like to express a profound sense of gratitude towards Prof. Tatwadarshi P. N., Department of Computer Engineering for his
constant encouragement and valuable suggestions. The work that we have been able to present is possible because of his timely guidance
and support.
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