Lexicon Based Emotion Analysis on Twitter Dataijtsrd
This paper presents a system that extracts information from automatically annotated tweets using well known existing opinion lexicons and supervised machine learning approach. In this paper, the sentiment features are primarily extracted from novel high coverage tweet specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment word hashtags and from tweets with emoticons. The sentence level or tweet level classification is done based on these word level sentiment features by using Sequential Minimal Optimization SMO classifier. SemEval 2013 Twitter sentiment dataset is applied in this work. The ablation experiments show that this system gains in F Score of up to 6.8 absolute percentage points. Nang Noon Kham "Lexicon Based Emotion Analysis on Twitter Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26566.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26566/lexicon-based-emotion-analysis-on-twitter-data/nang-noon-kham
This document summarizes a research paper that proposes a method for performing sentiment analysis on product reviews to identify promising product features. It involves scraping short reviews from websites, preprocessing the text through cleaning, tokenization and part-of-speech tagging. Next, it uses pattern mining and a custom lexicon dictionary to determine the overall sentiment score and sentiment scores for specific product features. The goal is to analyze which features consumers view most positively to help businesses understand customer preferences.
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
The document presents a major project on developing a system called Tweezer to analyze tweets and determine if they have a positive or negative sentiment. It discusses the background of the project, objectives, features of Tweezer, methodology using naïve Bayes classification, and results. The system was able to analyze tweets and represent the results in graphs, but had limitations such as only analyzing 25 tweets and not determining neutral tweets. Future work could improve on determining sentiment of emojis and expanding the analysis capabilities.
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.
IRJET- Sentimental Analysis of Product Reviews for E-Commerce WebsitesIRJET Journal
This document summarizes a research paper that proposes using sentiment analysis of product reviews on e-commerce websites to help consumers decide where to purchase a product. The researchers describe collecting reviews from multiple websites, preprocessing the text, using clustering and classification algorithms like mean shift and support vector machines to label reviews as positive, negative or neutral. The system would then compare the results across websites and recommend the one with the most positive reviews to reduce the time users spend researching. Future work could include detecting fake reviews and identifying reasons for negative reviews on particular sites.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
Lexicon Based Emotion Analysis on Twitter Dataijtsrd
This paper presents a system that extracts information from automatically annotated tweets using well known existing opinion lexicons and supervised machine learning approach. In this paper, the sentiment features are primarily extracted from novel high coverage tweet specific sentiment lexicons. These lexicons are automatically generated from tweets with sentiment word hashtags and from tweets with emoticons. The sentence level or tweet level classification is done based on these word level sentiment features by using Sequential Minimal Optimization SMO classifier. SemEval 2013 Twitter sentiment dataset is applied in this work. The ablation experiments show that this system gains in F Score of up to 6.8 absolute percentage points. Nang Noon Kham "Lexicon Based Emotion Analysis on Twitter Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26566.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26566/lexicon-based-emotion-analysis-on-twitter-data/nang-noon-kham
This document summarizes a research paper that proposes a method for performing sentiment analysis on product reviews to identify promising product features. It involves scraping short reviews from websites, preprocessing the text through cleaning, tokenization and part-of-speech tagging. Next, it uses pattern mining and a custom lexicon dictionary to determine the overall sentiment score and sentiment scores for specific product features. The goal is to analyze which features consumers view most positively to help businesses understand customer preferences.
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
The document presents a major project on developing a system called Tweezer to analyze tweets and determine if they have a positive or negative sentiment. It discusses the background of the project, objectives, features of Tweezer, methodology using naïve Bayes classification, and results. The system was able to analyze tweets and represent the results in graphs, but had limitations such as only analyzing 25 tweets and not determining neutral tweets. Future work could improve on determining sentiment of emojis and expanding the analysis capabilities.
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.
IRJET- Sentimental Analysis of Product Reviews for E-Commerce WebsitesIRJET Journal
This document summarizes a research paper that proposes using sentiment analysis of product reviews on e-commerce websites to help consumers decide where to purchase a product. The researchers describe collecting reviews from multiple websites, preprocessing the text, using clustering and classification algorithms like mean shift and support vector machines to label reviews as positive, negative or neutral. The system would then compare the results across websites and recommend the one with the most positive reviews to reduce the time users spend researching. Future work could include detecting fake reviews and identifying reasons for negative reviews on particular sites.
This document discusses various techniques for sentiment analysis of application reviews, including both statistical and natural language processing approaches. It describes how sentiment analysis can be used to analyze textual reviews and classify them as positive or negative. Several key techniques are discussed, such as using machine learning classifiers like Naive Bayes, extracting n-grams and sentiment-oriented words, and developing rule-based models using techniques like identifying parts of speech. The document also discusses using these techniques to perform sentiment analysis at both the document and aspect levels.
This document presents a project report for a Master's thesis on opinion mining and sentiment analysis. The report includes an abstract, acknowledgments, table of contents, and chapters covering the project overview and background on opinion mining, sentiment analysis, the project requirements and architecture, relevant technologies, the project design and implementation, approaches to sentiment analysis, and conclusions. The project aims to classify user comments from a major social site based on sentiment analysis.
This document discusses using sentiment analysis to predict project performance by analyzing language in project reports and communications. It proposes focusing the analysis on select correspondence between key project members, periodic structured reports containing issues/risks, and narrative management reports. Conducting a narrow sentiment analysis of reliable, high-confidence data sources from within the project domain can improve predictive capabilities over broad analyses by increasing the signal-to-noise ratio and computational efficiency. The meaning of words can depend on context, so sentiment analysis may need to consider the applicable contexts more narrowly when including a broader range of project text.
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.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
The document summarizes research on aspect-based sentiment analysis. It discusses four main tasks in aspect-based sentiment analysis: aspect term extraction, aspect term polarity identification, aspect category detection, and aspect category polarity identification. It then reviews several approaches researchers have used for each task, including supervised methods like conditional random fields and support vector machines, as well as unsupervised methods. The document concludes by comparing results from different studies on restaurant and laptop review datasets.
opinion feature extraction using enhanced opinion mining technique and intrin...INFOGAIN PUBLICATION
Mining patterns are the main source of opinion feature extraction techniques, which was individually evaluated corpus mostly belong to evaluated corpus. A measure called Domain Relevance is used to identify candidate features from domain dependent and domain independent corpora both. Opinion Features originated are relevant to a domain. For every extracted candidate feature its individual Intrinsic Domain Relevance and Extrinsic Domain Relevance values are registered. Threshold has been compared with these values and recognizes as best candidate features. In this thesis, By applying feature filter creation the features from online reviews can be identified .
Temporal Exploration in 2D Visualization of Emotions on Twitter StreamTELKOMNIKA JOURNAL
This document presents a system for visualizing emotions expressed on Twitter streams over time from different geographic locations. The system collects tweets from the US, Japan, Indonesia, and Taiwan related to iPhones and performs sentiment analysis using naive Bayes classification. It then visualizes the results using two-dimensional heat maps, interactive stream graphs, and context focus brushing. The visualizations allow exploration of temporal patterns in customer emotional behavior across locations expressed in Twitter data.
Opinion Mining Techniques for Non-English Languages: An OverviewCSCJournals
The amount of user-generated data on web is increasing day by day giving rise to necessity of automatic tools to analyze huge data and extract useful information from it. Opinion Mining is an emerging area of research concerning with extracting and analyzing opinions expressed in texts. It is a language and domain dependent task having number of applications like recommender systems, review analysis, marketing systems, etc. Early research in the field of opinion mining has concentrated on English language. Many opinion mining tools and linguistic resources have been built for English language. Availability of information in regional languages has motivated researchers to develop tools and resources for non-English languages. In this paper we present a survey on the opinion mining research for non-English languages.
Sentiment Analysis Using Hybrid Approach: A SurveyIJERA Editor
Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressions that are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing.
This document summarizes a research project on sentiment analysis of tweets about news. The researchers collected tweets related to news articles from various sources and analyzed the sentiment of the tweets to determine the overall public sentiment toward that news. They first preprocessed the tweet text through tokenization, removed stopwords, and calculated term frequencies. Next, they analyzed term co-occurrences to understand context. They also created visualizations of frequent terms. Finally, they used a naive Bayes classifier trained on labeled data to classify tweets in real-time as positive, negative, or neutral sentiment toward the news. The system aimed to provide a score indicating overall public sentiment toward each news article based on related tweets.
A Survey Of Collaborative Filtering Techniquestengyue5i5j
This document provides a survey of collaborative filtering techniques. It begins with an introduction to collaborative filtering and its main challenges, such as data sparsity, scalability, and synonymy. It then describes three main categories of collaborative filtering techniques: memory-based, model-based, and hybrid approaches. Representative algorithms from each category are discussed and analyzed in terms of their predictive performance and ability to address collaborative filtering challenges. The document concludes with a discussion of evaluating collaborative filtering algorithms and commonly used datasets.
This document summarizes a research paper on opinion mining from Twitter data. It discusses the challenges of sentiment analysis on short Twitter posts, including named entity recognition, anaphora resolution, parsing, and detecting sarcasm. It also reviews several papers on related topics, such as frameworks for Twitter opinion mining using classification techniques, using Twitter as a corpus for sentiment analysis, and analyzing opinions during the 2012 Korean presidential election on Twitter. Overall, it covers key techniques in opinion mining like identifying opinion targets and orientation. It proposes future work to develop a web application to compare Twitter opinion mining performance and use supervised learning to improve accuracy.
In this paper, we present three techniques for incorporating syntactic metadata in a textual retrieval system. The first technique involves just a syntactic analysis of the query and it generates a different weight for each term of the query, depending on its grammar category in the query phrase. These weights will be used for each term in the retrieval process. The second technique involves a storage optimization of the system's inverted index that is the inverse index will store only terms that are subjects or predicates in the document they appear in. Finally, the third technique builds a full syntactic index, meaning that for each term in the term collection, the inverse index stores besides the term-frequency and the inverse-document-frequency, also the grammar category of the term for each of its occurrences in a document.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
With the rapidly increasing growth in the field of internet and web usage, it has become essential to use a certain specific powerful tool, which should be capable to analyze and rank all these available reviews/opinion on the web/Internet. In this paper we have propose a new and effective approach which uses a powerful sentiment analysis procedure which will be based on an ontological adjustment and arrangements. This study also aims to understand pos tag order to get detailed observation for any review or opinion, it also helps in identifying all present positive /Negative sentiments and suggest a proper sentence inclination. For this we have used reviews available on internet regarding Nokia and Stanford parser for the purpose or pos tagging.
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.
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.
This document summarizes a survey of opinion mining and sentiment analysis techniques. It discusses how opinion mining uses natural language processing and machine learning to analyze sentiment in text sources like blogs, reviews and social media. It outlines several key tasks in opinion mining including sentiment classification at the document, sentence and feature levels. Supervised, unsupervised and semi-supervised machine learning algorithms are commonly used for sentiment classification tasks. Naive Bayes classification and text classification algorithms are also discussed.
EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVEijsc
This document presents the results of a bibliometric analysis of 523 research articles on sentiment analysis in social media published between 2018 and 2022. Key findings include:
1) Publication of articles on the topic significantly increased over time, with the highest number (789) published in 2021. Popular trend topics included "sentiment analysis" and "social networking (online)."
2) India contributed the most publications (224 articles), followed by China (18 articles) and the United States (15 articles). Indian authors also demonstrated high levels of international collaboration.
3) Emerging topics in abstracts included "deep learning," "social media," and "classification of information." The study aims to identify influential agents and publication trends within
EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVEijsc
Sentiment analysis has been rapidly employed for business decision support. New data mining researchers
are yet to have an adequate understanding of the various applications of sentiment analysis while utilising
social media data. As a result, it is critical to define the data mining and text analytics research trend
holistically using existing literature. The study explores sentiment analysis research for its application in
transforming social media data and identifies relevant research aspects through a comprehensive
bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to
discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis
are mainly related to innovation, transparency, and efficiency. Our review also highlights the
distinctiveness of sentiment analysis for synthesising social media information to investigate various
features, including the knowledge-domain map that detects author collaboration networks in the past.
A large-scale sentiment analysis using political tweetsIJECEIAES
Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extreme political opinions words, and features in tweets. Political multi-class sentiment analysis (PMSA) system on the big data platform is developed to predict the inclination of tweets to infer the results of the elections by conducting the analysis on different political datasets: including the Trump election dataset and the BBC News politics. The comparative analysis is the experimental results which are better political text classification by using the three different models (multinomial naïve Bayes (MNB), decision tree (DT), linear support vector classification (SVC)). In the comparison of three different models, linear SVC has the better performance than the other two techniques. The analytical evaluation results show that the proposed system can be performed with 98% accuracy in linear SVC.
IRJET - Sentiment Analysis for Marketing and Product Review using a Hybrid Ap...IRJET Journal
This document presents a hybrid approach for sentiment analysis that combines a lexicon-based technique and a machine learning technique using recurrent neural networks. It aims to analyze sentiments expressed in tweets towards products and services more accurately. The proposed model first cleans tweets collected from Twitter APIs. It then classifies the tweets' sentiment using both a lexicon-based technique using TextBlob and an LSTM-RNN model. The hybrid approach provides not only classification of sentiment but also a score of sentiment strength. This combined approach seeks to gain deeper insights than single techniques alone.
This document discusses using sentiment analysis to predict project performance by analyzing language in project reports and communications. It proposes focusing the analysis on select correspondence between key project members, periodic structured reports containing issues/risks, and narrative management reports. Conducting a narrow sentiment analysis of reliable, high-confidence data sources from within the project domain can improve predictive capabilities over broad analyses by increasing the signal-to-noise ratio and computational efficiency. The meaning of words can depend on context, so sentiment analysis may need to consider the applicable contexts more narrowly when including a broader range of project text.
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.
This document summarizes a dissertation submitted for the degree of Bachelor of Technology in Computer Science and Engineering. The dissertation analyzes sentiment of mobile reviews using supervised learning methods like Naive Bayes, Bag of Words, and Support Vector Machine. Five students conducted the research under the guidance of an internal guide. The document includes sections on introduction, literature survey of models used, system analysis and design including software and hardware requirements, implementation details, testing strategies and results. Screenshots of the three supervised learning methods are also provided.
The document summarizes research on aspect-based sentiment analysis. It discusses four main tasks in aspect-based sentiment analysis: aspect term extraction, aspect term polarity identification, aspect category detection, and aspect category polarity identification. It then reviews several approaches researchers have used for each task, including supervised methods like conditional random fields and support vector machines, as well as unsupervised methods. The document concludes by comparing results from different studies on restaurant and laptop review datasets.
opinion feature extraction using enhanced opinion mining technique and intrin...INFOGAIN PUBLICATION
Mining patterns are the main source of opinion feature extraction techniques, which was individually evaluated corpus mostly belong to evaluated corpus. A measure called Domain Relevance is used to identify candidate features from domain dependent and domain independent corpora both. Opinion Features originated are relevant to a domain. For every extracted candidate feature its individual Intrinsic Domain Relevance and Extrinsic Domain Relevance values are registered. Threshold has been compared with these values and recognizes as best candidate features. In this thesis, By applying feature filter creation the features from online reviews can be identified .
Temporal Exploration in 2D Visualization of Emotions on Twitter StreamTELKOMNIKA JOURNAL
This document presents a system for visualizing emotions expressed on Twitter streams over time from different geographic locations. The system collects tweets from the US, Japan, Indonesia, and Taiwan related to iPhones and performs sentiment analysis using naive Bayes classification. It then visualizes the results using two-dimensional heat maps, interactive stream graphs, and context focus brushing. The visualizations allow exploration of temporal patterns in customer emotional behavior across locations expressed in Twitter data.
Opinion Mining Techniques for Non-English Languages: An OverviewCSCJournals
The amount of user-generated data on web is increasing day by day giving rise to necessity of automatic tools to analyze huge data and extract useful information from it. Opinion Mining is an emerging area of research concerning with extracting and analyzing opinions expressed in texts. It is a language and domain dependent task having number of applications like recommender systems, review analysis, marketing systems, etc. Early research in the field of opinion mining has concentrated on English language. Many opinion mining tools and linguistic resources have been built for English language. Availability of information in regional languages has motivated researchers to develop tools and resources for non-English languages. In this paper we present a survey on the opinion mining research for non-English languages.
Sentiment Analysis Using Hybrid Approach: A SurveyIJERA Editor
Sentiment analysis is the process of identifying people’s attitude and emotional state’s from language. The main objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Opinion mining, also known as Sentiment analysis, plays an important role in this process. It is the study of emotions i.e. Sentiments, Expressions that are stated in natural language. Natural language techniques are applied to extract emotions from unstructured data. There are several techniques which can be used to analysis such type of data. Here, we are categorizing these techniques broadly as ”supervised learning”, ”unsupervised learning” and ”hybrid techniques”. The objective of this paper is to provide the overview of Sentiment Analysis, their challenges and a comparative analysis of it’s techniques in the field of Natural Language Processing.
This document summarizes a research project on sentiment analysis of tweets about news. The researchers collected tweets related to news articles from various sources and analyzed the sentiment of the tweets to determine the overall public sentiment toward that news. They first preprocessed the tweet text through tokenization, removed stopwords, and calculated term frequencies. Next, they analyzed term co-occurrences to understand context. They also created visualizations of frequent terms. Finally, they used a naive Bayes classifier trained on labeled data to classify tweets in real-time as positive, negative, or neutral sentiment toward the news. The system aimed to provide a score indicating overall public sentiment toward each news article based on related tweets.
A Survey Of Collaborative Filtering Techniquestengyue5i5j
This document provides a survey of collaborative filtering techniques. It begins with an introduction to collaborative filtering and its main challenges, such as data sparsity, scalability, and synonymy. It then describes three main categories of collaborative filtering techniques: memory-based, model-based, and hybrid approaches. Representative algorithms from each category are discussed and analyzed in terms of their predictive performance and ability to address collaborative filtering challenges. The document concludes with a discussion of evaluating collaborative filtering algorithms and commonly used datasets.
This document summarizes a research paper on opinion mining from Twitter data. It discusses the challenges of sentiment analysis on short Twitter posts, including named entity recognition, anaphora resolution, parsing, and detecting sarcasm. It also reviews several papers on related topics, such as frameworks for Twitter opinion mining using classification techniques, using Twitter as a corpus for sentiment analysis, and analyzing opinions during the 2012 Korean presidential election on Twitter. Overall, it covers key techniques in opinion mining like identifying opinion targets and orientation. It proposes future work to develop a web application to compare Twitter opinion mining performance and use supervised learning to improve accuracy.
In this paper, we present three techniques for incorporating syntactic metadata in a textual retrieval system. The first technique involves just a syntactic analysis of the query and it generates a different weight for each term of the query, depending on its grammar category in the query phrase. These weights will be used for each term in the retrieval process. The second technique involves a storage optimization of the system's inverted index that is the inverse index will store only terms that are subjects or predicates in the document they appear in. Finally, the third technique builds a full syntactic index, meaning that for each term in the term collection, the inverse index stores besides the term-frequency and the inverse-document-frequency, also the grammar category of the term for each of its occurrences in a document.
A HYBRID CLASSIFICATION ALGORITHM TO CLASSIFY ENGINEERING STUDENTS’ PROBLEMS ...IJDKP
The social networking sites have brought a new horizon for expressing views and opinions of individuals.
Moreover, they provide medium to students to share their sentiments including struggles and joy during the
learning process. Such informal information has a great venue for decision making. The large and growing
scale of information needs automatic classification techniques. Sentiment analysis is one of the automated
techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to
classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful
to draw decisions in education system since they classify the sentiments into merely three pre-set
categories: positive, negative and neutral. Moreover, classifying the students’ sentiments into positive or
negative category does not provide deeper insight into their problems and perks. In this paper, we propose
a novel Hybrid Classification Algorithm to classify engineering students’ sentiments. Unlike traditional
predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process
descriptive. Moreover, it classifies engineering students’ perks in addition to problems into several
categories to help future students and education system in decision making.
With the rapidly increasing growth in the field of internet and web usage, it has become essential to use a certain specific powerful tool, which should be capable to analyze and rank all these available reviews/opinion on the web/Internet. In this paper we have propose a new and effective approach which uses a powerful sentiment analysis procedure which will be based on an ontological adjustment and arrangements. This study also aims to understand pos tag order to get detailed observation for any review or opinion, it also helps in identifying all present positive /Negative sentiments and suggest a proper sentence inclination. For this we have used reviews available on internet regarding Nokia and Stanford parser for the purpose or pos tagging.
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.
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.
This document summarizes a survey of opinion mining and sentiment analysis techniques. It discusses how opinion mining uses natural language processing and machine learning to analyze sentiment in text sources like blogs, reviews and social media. It outlines several key tasks in opinion mining including sentiment classification at the document, sentence and feature levels. Supervised, unsupervised and semi-supervised machine learning algorithms are commonly used for sentiment classification tasks. Naive Bayes classification and text classification algorithms are also discussed.
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1) Publication of articles on the topic significantly increased over time, with the highest number (789) published in 2021. Popular trend topics included "sentiment analysis" and "social networking (online)."
2) India contributed the most publications (224 articles), followed by China (18 articles) and the United States (15 articles). Indian authors also demonstrated high levels of international collaboration.
3) Emerging topics in abstracts included "deep learning," "social media," and "classification of information." The study aims to identify influential agents and publication trends within
EXPLORING SENTIMENT ANALYSIS RESEARCH: A SOCIAL MEDIA DATA PERSPECTIVEijsc
Sentiment analysis has been rapidly employed for business decision support. New data mining researchers
are yet to have an adequate understanding of the various applications of sentiment analysis while utilising
social media data. As a result, it is critical to define the data mining and text analytics research trend
holistically using existing literature. The study explores sentiment analysis research for its application in
transforming social media data and identifies relevant research aspects through a comprehensive
bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to
discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis
are mainly related to innovation, transparency, and efficiency. Our review also highlights the
distinctiveness of sentiment analysis for synthesising social media information to investigate various
features, including the knowledge-domain map that detects author collaboration networks in the past.
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The sarcasm detection with the method of logistic regressionEditorIJAERD
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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
Sentiment analysis in SemEval: a review of sentiment identification approachesIJECEIAES
Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, sentiment analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.
A scalable, lexicon based technique for sentiment analysisijfcstjournal
Rapid increase in the volume of sentiment rich social media on the web has resulted in an increased
interest among researchers regarding Sentimental Analysis and opinion mining. However, with so much
social media available on the web, sentiment analysis is now considered as a big data task. Hence the
conventional sentiment analysis approaches fails to efficiently handle the vast amount of sentiment data
available now a days. The main focus of the research was to find such a technique that can efficiently
perform sentiment analysis on big data sets. A technique that can categorize the text as positive, negative
and neutral in a fast and accurate manner. In the research, sentiment analysis was performed on a large
data set of tweets using Hadoop and the performance of the technique was measured in form of speed and
accuracy. The experimental results shows that the technique exhibits very good efficiency in handling big
sentiment data sets.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
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Dialectal Arabic sentiment analysis based on tree-based pipeline optimizatio...IJECEIAES
This document summarizes a research paper that proposes using a tree-based pipeline optimization tool (TPOT) to improve sentiment classification of dialectal Arabic texts. The paper provides background on sentiment analysis and challenges in analyzing informal Arabic texts. It then discusses related work applying TPOT and AutoML techniques to optimize machine learning for various tasks. The proposed approach uses TPOT for sentiment analysis of three Arabic dialect datasets to automatically optimize hyperparameters and improve over similar prior work.
Insights to Problems, Research Trend and Progress in Techniques of Sentiment ...IJECEIAES
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Sentiment Analysis in Social Media and Its OperationsIRJET Journal
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FEATURE SELECTION AND CLASSIFICATION APPROACH FOR SENTIMENT ANALYSISmlaij
The document describes a proposed model for sentiment analysis of movie reviews using natural language processing and machine learning approaches. The model first applies various data pre-processing techniques to the dataset, including tokenization, pruning, filtering tokens, and stemming. It then investigates the performance of classifiers like Naive Bayes and SVM combined with different feature selection schemes, including term occurrence, binary term occurrence, term frequency and TF-IDF. Experiments are run using n-grams up to 4-grams to determine the best approach for sentiment analysis.
APPROXIMATE ANALYTICAL SOLUTION OF NON-LINEAR BOUSSINESQ EQUATION FOR THE UNS...mathsjournal
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equation under Dupuit assumptions is a nonlinear partial differential equation. In the present paper
approximate analytical solution of nonlinear Boussinesq equation is obtained using Homotopy
perturbation transform method(HPTM). The solution is compared with the exact solution. The
comparison shows that the HPTM is efficient, accurate and reliable. The analysis of two important aquifer
parameters namely viz. specific yield and hydraulic conductivity is studied to see the effects on the height
of water table. The results resemble well with the physical phenomena.
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Sentiment Analysis Tasks and Approaches
1. 1. Systems & Information Department, Engineering Research Division, National Research Centre, Dokki, Cairo 12311, Egypt.
Email: enaskhalil@gmail.com
2. Ain Shams University, Faculty of Engineering, Computers & Systems Department, Egypt.
Sentiment Analysis Tasks and Approaches
Enas A. H. Khalil1
, Enas M.F. El Houby 1
and Hoda K. Mohamed 2
Abstract
Sentiment analysis (SA) or opinion mining extracts and analyses subjective information from various sources such as the
web, social media, and other sources to determine people's opinions using natural language processing (NLP),
computational linguistics, and text analysis. This analyzed information gives the public's feelings or attitudes about
specific items, persons, or ideas and identifies the information's contextual polarity. This systematic review gives a clear
image of recent work in sentiment analysis SA; it studies the papers published in the SA field between 2016 and 2020
using the science direct and Springer databases. Furthermore, it explains the various approaches employed and the
various uses of SA systems. In science Direct, 99 publications meet our research requirements, whereas, in Springer, 57
papers meet the same conditions, with a total of 156 papers reviewed and assessed in this systematic review. Techniques,
performance, language, and the domain have been analyzed.
Keywords
Sentiment analysis; opinion mining; Natural Language Processing; Machine Learning; Deep Learning; word embedding.
I. INTRODUCTION
Sentiment analysis or opinion mining is a computational field that studies people's opinions, sentiments, emotions,
ratings, and attitudes towards objects such as products, services, organizations, persons, events, topics, and attributes
[1]. Generally, text information can be classified into two main types: facts and opinions. Facts are objective
expressions about something. Opinions are usually subjective expressions that describe people's sentiments,
speculations, and feelings toward a subject or topic.
Currently, sentiment analysis has many practical applications due to the rapid growth rate of information over the
internet; many texts express opinions on review sites, forums, blogs, and social media. Opinions are the basis of
almost all human activities and stimulate our behaviors. We have recently noticed that opinionated postings on
social media have helped businesses reshape and control public sentiments and emotions, profoundly impacting our
political and social organizations. Finding and controlling opinion sites on the web and filtering the information
contained in them remains challenging because of the propagation of different sites. Each site typically contains a
large volume of opinion text that is not easily decoded in long blogs and forum postings. Therefore, the regular
reader will find it difficult to identify relevant sites and get and summarize the opinions involved, so the need for
automated sentiment analysis systems arises. Current researches have created different techniques for different tasks
of SA, using either supervised or unsupervised methods. Early articles used all types of supervised machine learning
techniques (such as Support Vector Machines (SVM), Maximum Entropy (MaxEnt), and Naïve Bayes (NB) and
Unsupervised techniques include various sentiment lexicons, syntactic patterns, and grammatical analysis [1][2].
The interest in neural networks was feeble till the late 1990s as they only considered practical with one- or two-
layers networks "shallow." Training a neural network with more layers "deep" is computationally very expensive
and complicated. Deep Learning (DL) approaches emerged in the last few years as solid computational models
capable of autonomously revealing incredibly complex semantic representations of texts from data without any
2. feature engineering. These DL techniques are now used in many application fields, as computer vision, speech
recognition, and NLP applications such as sentiment analysis tasks[2, 3].
The sentiment analysis system has been classified on a data-level basis into three distinct levels: document,
sentence, and aspect. Document-level sentiment analysis (DLSA) classifies the whole opinionated document. A
single information unit is represented by a document that provides ideas or thoughts about a particular subject.
Sentence level Sentiment analysis (SLSA) classifies each sentence in a document. A sentence is first classified as
opinionated or non-opinionated in a process known as subjectivity categorization. After that, the resultant
opinionated phrases are labeled as expressing either positive or negative opinions.
Aspect level sentiment analysis, also referred to as aspect-based sentiment analysis (ABSA), is a more fine-grained
method than DLSA and SLSA. It extracts and aggregates public perceptions about entities and their associated
aspects/features, referred to as targets. With aspect-based sentiment analysis, businesses may pick up on the details
of specific traits, components, or entities, identifying what customers like and hate. For instance, "The meal was
delicious, but the service was bad," as stated in a restaurant review. In this statement, we have two things, "meal"
and "service," each with two matching characteristics, "delicious " and "bad." The process of aspect sentiment
classification is to classify the sentiment on the food as positive and on the service as negative. Relating the target
with its surrounding context words is hard, making ABSA a challenging task; different context words affect the
sentiment polarity towards the target. So capturing semantic connections between the target word and context is a
must in building learning models [4].
The contribution of this survey is to provide categorization of a large number of recent articles according to the
algorithm used, which can help the researchers in choosing the appropriate one for a particular application and give
them the ability to investigate tasks and the evaluation of resources built and used in this field. Then, the accessible
benchmark data sets are classified by domain. Performance results are also mentioned for each article to help
compare the improvement in performance according to different algorithms used.
The remainder of the research paper is structured as follows: Section 2 provides the methodology for the review,
which includes subsections on research criteria and data extraction. Section 3 browsed the results; it is divided into
five different subsections; the first subsection covers the various languages utilized, then the various functions
performed by SA systems are browsed in the second subsection such as general-purpose SA, SA for emotion
detection, and SA for judging the improvement in feature selection. Also, different Sentiment Classification (SC)
approaches used throughout our study are briefly discussed in the third subsection, then datasets and data domains
are presented in the fourth subsection. The final subsection examined performance metrics in the studied research
papers. Finally, the article is concluded, and Section 4 discusses future research directions.
METHODOLOGY
A. Research Criteria
This Systematic Review aims to identify various studies using sentiment analysis systems based on different
classification techniques, and it focuses on the following:
The different techniques in SC.
3. The Categorizations of different SA systems according to the language studied
The task or scope of the studied SA systems.
The data domains are used for the development of SA systems.
The performance evaluation of different SA systems.
Science Direct database (Elsevier) (https://www.sciencedirect.com) and Springer database
(https://www.springer.com) were searched. The following search keywords were used: "sentiment analysis,"
"machine learning," and "Deep Learning." The published articles from 2016 to July 2020 were analyzed. However,
some relevant studies may have been dropped unintentionally.
Not all linked publications were examined; instead, only those that met the following inclusion criteria were
considered:
The research articles based on the previously mentioned keywords only included; the surveys, reviews, and
empirical studies are excluded.
(3) The research articles that mainly used text data sets, audio or video, were not included.
(4) All research articles must be fully complete (abstracts only or posters are excluded).
(5) Work published in the period 2016 to July 2020.
Figure 1 depicts the number of papers published in the "Science Direct" and "Springer" databases between 2016 and
July 2020 that meet our study requirements. Thus, the SA subject is still a popular study area, as seen by the linear
increase in published articles that meet our requirements, even in 2020 with COVID-19 circumstances that impede
the publication of new research, indicating that the research in that field is still expanding.
B. Data Extraction
The number of retrieved papers was 114 and 73 articles from Science Direct and Springer, respectively, then
papers that do not match the inclusion criteria have been removed. So, only 99, 57 papers from Science Direct and
Springer respectively have been included, with 156 from 187. Table A in the appendix shows samples of studied
articles in Science Direct and Springer. The first column mentioned the reference number. The language used for the
SA application is stated in column two. The classification algorithm used is stated in column three. The purpose of
SA is stated in column four.
Figure 1. : Publications in sentiment analysis that met our study criteria between 2016 and July 2020 in (a) Science Direct (orange), Springer (blue)
4. II. RESULTS
This section details the results obtained from the collected data and their analysis.
A. Language
Figure 2 illustrates the various languages involved in the articles examined in both databases. The most often used
language for sentiment analysis systems is English. Around 70% of researched publications either in SD or SP are
English. Chinese and Arabic follow English, but with a gap, Arabic and Chinese SA account for around 10% of all
articles examined. Chinese research has been overgrown in the previous two years; this might be attributed to the
widespread use of the Chinese language on the web due to China's considerable trade with the rest of the world and
its enormous population. Examples of Chinese ABSA are browsed through [5], [6], and ABSA with the attention-
based mechanism in [7]. Chinese SA in stock market prediction is studied in [8]. Ref[9] concerns are collecting
sentiment information for Chinese SA using DL. Ref [10] studies Sentiment Word Co-occurrence and knowledge
pair feature extraction in Chinese SA. Arabic SA research is also promising but is limited due to few resources and
Lexicons and Arabic's complicated morphology. However, the Arabic language is one of the most popular internet
users, resulting in a growing interest in the research area of Arabic SA and resources.[11], [12] The analysis of
Arabic SA in various dialects, as well as the enhancement of Arabic ABSA, is addressed in [13], [14]. Developing
extensive and comprehensive Arabic lexicons is critical for the advancement of the discipline, articles [15], [16], and
[17] focused on building Arabic Lexicons. The importance of FS in Arabic SA is also studied in [18], [19]. SA in
other different languages is limited. Figure 3 illustrates the annual number of papers produced in each language.
Fig. 2: Languages in SA articles referenced in this study
5. Fig. 3 number of articles on Different languages per year a) in SD b) in SP
B. Different views in Sentiment Analysis:
There are two different interpretations of the article's goal as follows:
- It either concentrates on technical aspects of SA models, such as the process of developing SA models via the
use of various classification approaches, as in [20], [11], [66], [21], [22], [23], [24], [25]and [26]; In SA, the effect
of utilising various feature selection (FS) approaches was investigated [27], [28], [29], [30], [18] ,[19], and [31].
Building Resources (BRs) like lexicons, dictionaries, and databases are employed as primary resources in SA
systems, particularly in languages with few resources such as Arabic [15], [16], [17] and in Urdu Language [32].
Many publications investigate the impact of word/document representation in SA using various embedding
approaches as [33], [34], [35], [36], [37], [38], [39] , and [18]. Domain adaptation problem is investigated in [36],
[40], [41].
OR, different commercial and social applications of SA systems are studied through these articles as stock market
prediction in [42], [43], [44], [8], ABSA of products in [45],[46], [13], [14], [6], [47], [48], [49], [31], Opinion
summarization in [50], [51]. Ref [52] shows Sarcasm detection model using SA. Cyberbullying and hate speeches
detected using SA in [53], and many other applications.
C. Sentiment Classification Approaches:
The primary step in sentiment analysis is to classify the polarity of a given text at the document, sentence, or
feature/aspect level as positive, negative, or neutral. Also, sentiment classification is used to determine emotional
states such as "angry," "sad," and "happy." The approaches employed in an SC model may be divided into three
broad categories: machine learning (ML), lexicon-based (LB), and a hybrid model combining ML and LB. Figure 4
0
2
4
6
8
10
12
14
16
b) Springer
2016 2017 2018
6. shows the statistics of SC approaches in studied articles in both databases.
1 4 9
16
24
10 6
13
11
12
3 1
5
5
2
0
20
40
2016 2017 2018 2019 2020
b) Different ML approaches in SD
articles
DL TML LB
Fig. 4 statistics of SC approaches in studied articles
The few paragraphs below give a brief description of the categorization of SC techniques.
1. Machine Learning (ML) can be further subdivided into Supervised Machine Learning SML, Unsupervised
Machine Learning UML.
Supervised Machine Learning SML The dataset is divided into a training set and a testing set. The classifier learns
from the training data and builds a model that is later used in the classification task of the testing set. This approach
generally achieves higher accuracy than that of the unsupervised approach for sentiment analysis. However, it
requires building a large corpus (dataset) and labeling it manually by human experts.
Unsupervised machine learning UML extracts patterns from a dataset without using known or labeled outcomes as a
guiyde. Therefore, unlike supervised machine learning, unsupervised machine learning approaches cannot be
immediately applied to a regression or classification issue because the output data values are unknown. UML has
been divided into clustering and association; the most common example of unsupervised machine learning
algorithms are K-Means and Apriori Algorithm.
A mix of SML and UML is a Semi-Supervised Learning SSL. Algorithms that are based on SSL have both labeled
and unlabeled data sets. In SSL, an initial classifier can be obtained by including information previously extracted
from an existing sentiment lexicon into sentiment classifier model learning, referring to this information as labeled
instances and using them directly to limit the model's predictions on unlabeled instances using popularized
expectation criteria [54],[55], [56]. Both SML and UML are categorized into traditional machine learning TML or
deep learning DL.
1.1 Traditional Machine Learning (TML): Several algorithms in this category are either supervised or unsupervised.
SML as Support Vector Machine SVM, Bayesian network, Naïve Bayes (NB), decision tree (DT), maximum
entropy (MaxEnt), logistic regression, and k nearest neighbor (KNN). From the 157 articles studied here, 82 out of
157 research articles in both databases are based on TML, as shown in figure 4. SVM and NB are the most TML
algorithms used, as shown in figure 5.
1.2 Deep learning (DL): A growing field of study involves the encoding of supervised or unsupervised learning
features inside a hierarchical structure. DL has presented outstanding contributions in many applications as
7. computer vision, named-entity recognition, and speech recognition[57]. Since 2016, DL methods have been widely
employed in SA. This review consists of 80 out of 157 research articles in both databases based on different DL
techniques used either as a primary technique or as a part of a hybrid system. These articles are distributed as shown
in figure 4, which clarifies the increasing importance of deep learning. Multi-layers automatic feature representation
can be obtained using DL models [58]; [59]. DL is effective in extracting implicit semantic features, which aids in
domain transfer. We significantly minimized feature engineering, human involvement, and computation time by
incorporating deep learning algorithms into SA tasks. [60]. The most often used DL models are Convolutional
Neural Network (CNN), Recurrent Neural Network (RNN), and its variations LSTM, BiLSTM.
2. Lexicon-based approach: The lexicon-based method is usually implemented using unlabelled data to predict the
polarity. Because of their simplicity, scalability, and computational efficiency, these methods are mainly used to
solve general-purpose SA problems. However, they rely heavily on human work on annotating the data and
sometimes experience low coverage. The lexicon-based approach is further divided into dictionary-based and
corpus-based. The dictionary-based approach finds opinion seed words and then searches the dictionary of their
synonyms and antonyms. The corpus-based approach begins with a seed list of opinion words and then finds other
opinion words in a large corpus to help find opinion words with context-specific orientations. From the 157 articles
studied here, 19 out of 157 research articles in both databases are based on LB, as shown in figure 4. Articles [61],
[62], [39],[82], [63] and [32],are examples of superiority is SA using LB approach.
3. Hybrid approach: Arises from the combination of the lexicon-based approach with machine learning and is
proved to enhance the performance of SA systems as in [64], [46], [65], [35],[66],[67], [68]
D. Datasets and Domain
Table B summarizes the most frequently used data sets in conjunction with their related articles. The data can be
classified according to the domain as follows:
1- Social networking: Facebook, Twitter, microblogs as Sina, a Chinese microblog, Edinburgh corpus (ED), The
Stanford Sentiment corpus (STS), Sanders, SemEval2013 task2, SemEval2013, website Donanim Haber, a
prominent domain-specific blogs in Turkey and Wikipedia.
37
26 26
23
14 14
8 7 6 6
0
10
20
30
40
count
(a)Topten SC Techniquesused in studied SD articles
SVM CNN LSTM NB LR LB RF RNN DT RB
Fig. 5 SC Techniques used in studied articles (a) SD (b) SP
8. 2- Product Review: Includes data from many products, electronics, apparel, electronics, reviews from the
Google Play store, numerous SemEval jobs such as SemEval 2013 and 2014, and SenTube product review.
3- Service Review: Hotel reviews in Semeval 2016 Task 5, Restaurants, review from Trip Advisors web site,
booking web site, Chinese tourism review, and Massive Open Online Courses (MOOC) review sites.
4- Specific data: as Email spam dataset, Arabic Online Commentary (AOC) dataset, Banks comments, Online
surveys, SMS Banking survey, COAE natural disease dataset Chinese, data for a language that is not a
commonly used one as Urdu Language, PubMed abstracts, and Linguistic data consortium (LDC) parallel
data[69].
5- Movies review data: IMDB, MR, and RT datasets.
6- News: Articles from online news sources, book reviews, periodicals, short stories, and Wikipedia.
7- Health: Clinical publications, online healthcare forums, Drugs.com.
8- Business: Stock market as (https://stocktwits.com/), and financial information as the Bloomberg website.
E. Performance measures:
Performance measures used throughout articles in this review are: precision Pr, recall R, and F –score (F1) are
computed as follows:
Pr (pos) = , Pr (neg) = (1)
R(pos) = , R(neg)= (2)
F1(pos)=2*(P(pos)*R(pos)/(P(pos)+R(pos)), F1(neg)=2*(P(neg)*R(neg)/(P(neg)+R(neg)) (3)
Where Pr, R, F1, TP, FP, TN, and FN represent Precision, Recall, F1 score, true positives, false positives, true
negatives, and false negatives.
The area under the receiver operating characteristic curve (AUC or AUROC) is another essential accuracy
measure. It does not rely on the cut-off values of the posterior probabilities. AUC is defined as follows:
AUC (positive sentiment) = 0∫1 ∫ ( ) d ( ) = 0∫1 ( ) d ( ) (4)
AUC (negative sentiment) = 0∫1 d ( ) = 0∫1 d ( ) (5)
18, 21%
17, 19%
9, 10%
26, 30%
0, 0%
2, 2%
1, 1% 13, 15%
2, 2%
Different Data domains in
SP articles
product movie Business Social Health
books Specific Service News
Fig. 6: Different Domains of data
9. TP: True Positives, FN: False Negatives, FP: False Positives, TN: True Negatives, P: Positives (positive
sentiment), N: Negatives (negative sentiment). The values of the AUC range from 0.5 to 1. An AUC of 0.5 means
that the model cannot do better than a random selection, while a value of 1 indicates a perfect prediction.
A brief description of the most common algorithms that give the best performance throughout the articles are listed
in the following few paragraphs in descending order from the most significant number of papers used to the minor
no of papers:
- Support Vector Machine Classifiers (SVM): This supervised machine learning technique tends to determine linear
separators in the space, which can best separate the different classes by maximizing marginal hyperplane (MMH),
which will minimize the error. Text data are ideally suited for SVM classification because of the sparse nature of
the text. However, they tend to be correlated with one another and generally organized into linearly separable
categories[70]. SVM can construct a nonlinear decision surface in the original feature space by mapping the data
instances non-linearly to an inner product space. Then, the classes can be separated linearly with a hyperplane [71].
Examples for best performance results using SVM: in Ref [72], SA of Italian language achieves Acc of 91.58%,
while in Arabic [15] Acc 90%, ABSA Acc of 95.4% in [14], and the use of optimization for FS in addition to SVM
for SC enhance the Arabic Acc 95.93%in [19]. Acc of 91.64% in [73] for English using an ensemble of SVM and
NB, English SA Acc 91.67% in [74]
- Convolutional Neural Networks (CNNs): CNN's are modifications of feed-forward NNs with the following
properties: (i) convolutional layers: A CNN usually has one or more convolutional layers that build adjoining
locative features (hidden units); (ii) sparse connectivity: instead of having fully connected neurons, inputs of hidden
units in the layer l are from a subset of units in layer l–1 that have adjoining locative features; (iii) shared weights:
Units belonging to the same local features share the same weights (weight vector and bias); and (iv) Pooling: instead
of using all the local features at the next level, they have a pooling layer that computes either the average or the
minimum or the maximum of these features. For NLP tasks, convolutional layers release local features around a
window of a given sequence of words. In addition, they are often gathered to extract higher-level features [75]. 36
out of 157 articles (23%) used CNN in SC. Spanish ABSA obtains an Acc of 70.5 % in[46], SA for Thai children
stories achieves F1- score of 81.7% of using CNN in [76], Acc 89.5% in Arabic Algerian Dialect SA using CNN in
[16], Chinese SA in [77]gets 92.52% Acc, Short text SA [135]results in Acc 0.92 Micro-AUC 0.98 Macro-
AUC0.9.7
- Long Short-Term Memory LSTM: Artificial Neural Network (ANN) that contains direct cycles in their hidden
connections is a recurrent neural network (RNN). RNN can only process a finite number of sequences[78] due to the
diminishing gradient; Long short‐term memory (LSTM) networks are a variation of RNN with a memory cell that can
maintain states over long periods, defeating the long-distance dependencies problem of RNNs [79]. An LSTM is a
memory cell, ct, which is recurrently connected to itself. It multiplies using three components: an input gate it, a forget
gate ft, and an output gate ot. These gating vectors have a range of [0, 1]. The cell makes deliberate choices regarding
memory storage and when to access units via open and closed gates. Figure 5 indicates that LSTM and Bidirectional
LSTM (BiLSTM) were employed in SC in 40 out of 157 publications (about 25%) in both databases.
10. - In [33] SA using BiLSTM achieves F1- of 91.3%, also attention-based bi-directional long short-term memory
recurrent neural network AttBiLSTM used for Korean text SA [37] with Acc 91.95 to 92.66, F1 of 92.43 to 93.
Chinese text BiLSTM -SA gives Acc of 84.36% in [9] using Sina, a Chinese Microblog. In [144], the use of LSTM
with attention mechanism for English and Japanese SA achieved Japanese Acc of 87.2 English Acc of 73.7. LSTM is
used for Multilanguage SA as [147].
- Naïve Bayes NB: A supervised probabilistic approach that can predict a probability distribution over a set of classes,
given an observation of input, rather than only outputting the most likely class to which the observation should belong.
Probabilistic classifiers provide a valuable classification as a standalone classifier or combined with other classifiers
into ensembles. NB is widely used for text classification. It is based on the Bayes probability theorem in which the
posterior probability of class or given predictor is calculated.
The NB algorithm is used in about 31 of 157(about 20%) in studied articles. For example, [29] achieves Acc 88.0% to
99.89% for different datasets used, while Acc of 0.7, R of 0.35, Pr of 0.47 were obtained in Arabic tweets SA in
[63]. In [80] English SA using POS features and MNB classifier gives Acc of 74% Pr of 77%R74% F1-of 74%
- Rule-based Approach RB: In which a set of rules are used to model the data space. The left-hand side shows the
feature set expressed in a contrapuntal way, and the right-hand side is the class label. The conditions are based on the
term presence; absence is rarely used because it is not informative in sparse data. The most common criteria used in
forming rules are support and confidence [81]. Support is the number of all examples in the training data set which are
relevant to the rule. Confidence is the conditional probability that the rule's right-hand side is fulfilled if the left-hand
side is fulfilled [82]. The RB is used in about 8 of 157(about 5%) in studied articles. Here RB is the only approach for
SC in articles [83], [84]. In most cases, better performance is obtained when used within the ensemble of classifiers or
in conjunction with another classifier as RB+LB in[65]. Combining RB with CNN in English ABSA in [85] obtained
Pr 79.25%, R 88.45%, F1 83.24%, and Acc of 87% in laptop reviews.[86]ensemble (RB, NB, SVM, and RNTN)
achieves an F score of 94.49%
- Ensemble-Based Classifiers: They can be used to obtain better performance than using single learning algorithms. The
Homogeneous Ensemble of Neural Networks (HEN) (comprising probabilistic neural networks (PNN) and Back
Propagation Neural Networks (BPN)) has shown superior performance in [87] achieving correctness (Pr) 90.3%,
completeness 93%, effectiveness 91.5%, efficiency 90.1%. other ensemble classifiers as in [88], [89], [86], [24], [67]
are shown exceptional performance.
Figure 5 above shows the top ten algorithms that achieved the best performance results. The SVM algorithm is the
predominant one with the greatest number of papers in our study that achieves better performance in different SA
applications, followed by different DL algorithms as CNN, LSTM.
III. CONCLUSIONS AND FUTURE WORK
This systematic review summarizes recent advances in SA methods and applications. A total of 157 papers were
analyzed and summarized. These papers contribute to various disciplines by demonstrating how SA methods may be
used to various real-world problems. From a linguistic standpoint, we can infer that English is the most studied
language in SA applications. However, due to a shortage of resources, research in other languages continues to
11. develop. Despite its complexity and scarcity of materials, SA in the Arabic language is gaining the popularity of the
vast number of individuals who use Arabic on the internet and social media. SA has rapidly developed in the
Chinese language during the last two years. As a result, Chinese and Arabic were the second and third most
prevalent SA fields.
Different tasks are investigated, including the basic sentiment analysis tasks SLSA, ABSA, and DLSA, and those
other specific tasks rely on the essential tasks to achieve the goal such as stock market prediction, building
recommendation system in many data domains, opinion summarization, dialect classification, building resources and
to solve domain adaptation problem. Different data set domains are used throughout the studied articles. However,
social media sites and different microblogs take a significant portion due to their primary role in expressing opinions
or feelings about a specific topic or product. There are different techniques used in the SA task. SVM algorithms are
the most prominent TML and achieved the best results in many systems studied through this SR. DL techniques
have been growing very fast in the last few years, especially since 2016. There is no need for feature engineering
and a remarkable ability to treat vast amounts of data like that on the web and social media. DL algorithms as CNN
and LSTM achieved high-rank results in this SR.DL techniques in languages other than English, especially in
Arabic, are a promising field. The Arabic language complex features benefit more from DL and still need to be
tackled more deeply.
TABLE A SAMPLE OF STUDIED ARTICLES
Ref Language Algorithm Task
Science Direct
[90] English Multilayer ANN SA of a product review
[91] English LSTM, Bi-L STM, C-LSTM, and Tree-LSTMs SC using cascade architecture
[92] English CNN, RNN Decision-making to choose, design, and manufacture
Electric vehicles.
[93] English LSTM + attention layer SA with multiple attention
[88] English Ensemble of BiLSTM, attention Multi-domain SA.
[94] English NB, DT, RF, KNN, GRU, CNN, and three-way
convolutional recurrent neural network 3CRNN
SC of Drug reviews.
[95] English ASP-BRNN Extracting semantic information, enhancing the SA system
[96] English LB+recursive neural tensor network model SA for reviewing and extracting knowledge from a large
body of scientific literature
[97] English dilated convolutional neural network (D-CNN) Extraction of long-term contextual semantic features for SA,
[98] English 4-layer sequential NN SA of PubMed abstracts.
[99] English the gradient boosting trees SA of randomly sampled MOOCs and students' to predict
MOOC learner satisfaction and estimate their relative
effects.
[100] English CNN-LSTM Earn sentiment-specific vectors from CNN and LSTM.
[101] English Fuzzy rule-based, LB SA using fuzzy logic.
[102] English LSTM,MLP aspect-level sentiment classification (HHAS) using
hierarchical human-like strategy
[103] English target-dependent CNN ( TCNN ) target-level SA
[104] English RB, SVM, CNN and BiLSTM. Citation SC in clinical research publications
12. [105]
English Dynamic Architecture for Neural Networks
(DAN2) and SVM
Create a feature set for Twitter SA that is domain-
transferable.
[106] English LR SA of text comments of Bank customers
[107] English NB Improving the accuracy of decision support systems for SA
[108] English SVM
[109] English SVM, KNN, subspace discriminant (SSD), Tree SA for Prediction of venous thromboembolism
[110] English CNN Twitter text SA
[111] English (NB + ME) Performance comparison between Cross-ratio uninorm (NB
& ME), LB methods in SA
[112] English fuzzy logic+LB Hybrid sentence level SA
[113] English Ensemble LR, NB, LDA, LR, and SVM +soft
computing
Build a multiobjective weighted voting ensemble classifier
for text SC
[114] English Classification And Regression Tree CART, ANN,
Support Vector Regression (SVR) and Multiple
Linear Regressions (MLR)
[115] Chinese AttBiLSTM Fine-grained SC for the Chinese language using DL
[77] Chinese CNN Chinese text SA CNN
[13] Arabic NB, DT, KNN, SVM Enhancing ABSA for Arabic reviews
[15] Arabic CNN,LSTM,,NB,DT,RF, XGBoost, SVM Building a huge dataset for Arabic reviews.
[116] Arabic Combining CNN and LSTM models. Arabic SA using ensemble DL
Arabic Combining CNN and LSTM models. Arabic SA using ensemble DL
[39] Arabic LB Expanding an Arabic SL using a word embedding
[117] Malayalam NB,SVM,RF SA of Malayalam Tweets using TML
[118] Spanish Tree augmented naive Bayes (TAN) SA during critical events on two datasets in Spanish using
Bayesian networks
[119] Punjabi DNN Punjabi text SA using DL
[120] English,
Chinese
CHL-PRAE (RAE +Hownet lexicon) combination of RAE, LB for sentence-level SA
[121] English,
Chinese
lexicon integrated two-channel CNN–LSTM SA using CNN–LSTM and CNN–BiLSTM models with the
sentiment lexicon information
Springer
[122] English Ensemble of NB, SVM ,DT Building a deceptive detection model
[123] English LSTM+Attention mechanism Document SA using CSNN
[124] English Deep BiLSTM Analysis of aspect position information in ABSA
[125] English Linear SVC and LR SC using TF-IDF for FS
[48] English CNN ABSA with ontologies, CNN with stochastic parameter
optimization
[126] English Deep Feed-Forward NN A decision in tourism sector projects using SA
[127] English Neural network AutoRegressiveNNAR Inclusion of count predicators in SA for stock prediction
[128] English attention + LSTM stock closing price prediction using SA
[129] English recursive auto-encoders Recursive autoencoder for SA
[130] English DT+Genatic Algorithm+Swarm using two optimization algorithms and DT for SA
[131] English DT,RF,LinearRegressionWithSGD,
Lasso regression with SGD(LassoWithSGD),
(RidgeRegressionWithSGD), SVR
Embedding ontology features as lexical, semantic, and their
combination in SA
13. [132] English Fuzzy C-Means Big Data SC using Fuzzy C-means
[133] English NN, MLP prediction of abnormal stock return by SA
[134] English Paragraph Vector Using Weighting word Scheme in SA
[135] English Hierarchical Dirichlet processes (HDP)+ affinity
propagation (AP) algorithm
SA for Latent sentiment topic modeling
[136] Chinese RNN Chinese public figures opinion polling
[137] Chinese U-SVM(Universum SVM) Universum SVM –SC for better SA performance
[18] Arabic SVM DL(CNN+LSTM) for FS+use of FastText embedding
[19] Arabic SVM Use of Optimization for FS in Arabic SA
[138] Thai SVM+ rules error analyzes in SA for Thai children stories
[139] Thai SVM SA of Thai children stories
[140] Bangla an attention-based CNN SA with an attention mechanism
[7] English
Chinese
LSTM The significance degree values for various words in a phrase
are determined through the use of an attention-based process
in ABSA
[141] English,
Chinese
NB,KNN,LR,RF,DT,SVM, GBDT feature extraction methodology for SA of product reviews
[142] English,
Hungarian
SVM SA on social media over various genres and languages
[143] Turkish and
English
NBM,SVM,LR, DT Query expansion FS in SA
TABLE B: MOST COMMON DATASETS
Dataset Ref
Review data from Amazon https://www.amazon.com/ [54],[38], [72], [9],[144], [145], [87],[73], [146], [68], [156]
Social media (twitter ,Facebook,instgram) comments on different topics [21], [147], [148], [149], [20]
IMDB Movie Review, http://www.imdb.com/ [150] , [28, 38] , [89] , [86] , [151] , [147] , [152], [146],
[153],[ 156]
The movies dataset RT: http://www.rottentomatoes.com [38], [151]
SemEval-2015 [7]
MR- https://www.cs.cornell.edu/people/pabo/movie-review-data/ [150], [20], [35], [73], [146]
SST: The Stanford sentiment treebank [150], [11], [34] ,[38], [121], [146], [154]
Stanford-Sentiment140 corpus of 1,600,000 training tweets [155], [86], [17], [156], [157]
ASTD: Arabic Sentiment Tweets Dataset [116] , [158], [18]
SemEval 2013 [34],[86]
SemEval-2014 http://alt.qcri.org/semeval2014/task4 [86], [5], [45], [20], [159], [160],[7], [161]
SemEval-2016 [162], [23], [7], [163], [49], [153], [14], [13]
Tweet dataset collected by Dong et al. [164] [5], [45], [161]
Chinese datasets cover four domains: car, notebook, camera, and phone. [5], [20], [7]
Yelp Business at : https://www.yelp.com/dataset [165], [150], [146], [166]
Real Tweet Dataset: The health care reform (HCR) dataset in 2010. [55] , [23]
Real Tweet Dataset: Stanford sentiment gold standard (STS-Gold) [52],[74], [20]
TripAdvisor web site [148], [72] , [20], [114], [68]
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