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R. Pushpa, P. Priyadarshani, S. Santhosh Kumar
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- Student, Student, Student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet, Puducherry, Puducherry, Puducherry, India, India, India
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Abstract
nIn the current era, a thousand of people share their opinions every day on the well-known microblogging platform Twitter in the form of tweets. A tweet must be brief and straightforward in order to be effective. though sentiment analysis of Twitter data will be the main emphasis of this thesis. Sentiment analysis study encompasses NLP and text data mining. We will conduct sentiment analysis on Twitter data using several logistic machine learning approaches. Nonetheless, our attention will be directed toward methods and varieties of sentiment analysis in which we will learn how to retrieve tweets from Twitter. In addition, we will uncover some common metrics and compare various machine learning approaches on the same dataset.
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Keywords: Twitter, Sentiment Analysis (SA), Machine Learning, Logistic Regression, Positive, Negative, Natural Language Processing, TfidfVectorizer, Stemming.
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Software Engineering Tools & Technology Trends(josettt)]
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References
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- Pak, Alexander, Patrick Paroubek.Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREc. 2019;10.
- An Agarwal, B Xie, I Vovsha, O Rambow.Sentiment analysis of twitter data. Proceedings of the workshop on languages in social media. Association for Computational Linguistics, 2018.
- Khan, Farhan Hassan, Usman Qamar, and Saba Bashir A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet. Knowledge and Information Systems 2020.
- Narr, Sascha, Michael Hulfenhaus, Language-independent twitter sentiment analysis. Knowledge Discovery and Machine Learning (KDML), LWA .2018.
- Saif, Hassan, Yulan He, Semantic sentiment analysis of twitter. International Semantic Web Conference. Springer Berlin Heidelberg,2019.
- Da Silva, Nadia FF, Eduardo R., Tweet sentiment analysis with classifier ensembles. Decision Support Systems.2020;66:170-179.
- Jianqiang, G. Xiaolin, Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis, in IEEE Access, 2021;(5):2870-2879.
- Khan, Jawad, Byeong Soo Jeong. Summarizing customer review based on product feature and opinion. Machine Learning and Cybernetics (ICMLC), 2016 International Conference on. IEEE, 2019.
- International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-8 June, 2019, Sentiment Analysis Using Naïve Bayes Classifier Sentiment Analysis Using Naïve Bayes Classifier
- Sentiment Analysis using Maximum Entropy Algorithm in Big Data Durgesh Patel, Sakshi Saxena, Toran Verma, International Journal of Innovative Research in Science, Engineering and Technology (http://www.ijirset.com/upload/2016/may/246_49_Sentim ent.pdf)
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Journal of Software Engineering Tools & Technology Trends
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 29, 2024 | |
| Accepted | March 28, 2024 | |
| Published | April 5, 2024 |
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