Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance

Vidhya, R and Gopalakrishnan, Pavithra and Vallamkondu, Nanda Kishore (2021) Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

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Abstract

Sentiment analysis is a process that is a very popular concept nowadays because of the high volume of reviews, micro blogs, comments etc., generated in different sites like e-commerce and social networking sites. The main problem in the current system is, for users to know the polarity result of bulk data, which is very tough because users need to study and understand each review in terms of the polarity. Users are expecting a onetime result of the polarity of bulk reviews, comments, micro blogs etc. In social networking sites, users post their status or opinions to share to the world. In this category, Twitter is the most popular one. In twitter, users post many micro blogs related to a topic or crisis etc, and the topic may be linked with a greater number of micro blogs based on the keywords or hash tags used. In twitter, we can search for any topic with keywords or hash tags, and we get a bulk of responses of the users world-wide. If we want to know the exact opinions of the users, we need to analyze the data with sentiment analysis. Sentiment analysis is a concept of defining a statement as positive, neutral, or negative by analyzing words of the statement. Many concepts have been proposed for this requirement, and many sentidatasets have been prepared for this requirement. But by taking the advantages of Machine Learning we are proposing a concept of sentiment analysis in twitter using ML techniques. In this we use multiple ML techniques such a

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: svm nb tf-idf sentidatasets
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 21 Jun 2021 08:13
Last Modified: 21 Jun 2021 08:13
URI: https://eprints.eudl.eu/id/eprint/3956

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