Abstract
AbstractIn this paper, investigations of Sentiment Analysis over a well-known Social Media Twitter were done. As literatures show that some works related to Twitter Sentiment Analysis have been done and delivered interesting idea of features, but there is no a comparative study that shows the best features in performing Sentiment Analysis. In total we used 9 feature sets (41 attributes) that comprise punctuation, lexical, part of speech, emoticon, SentiWord lexicon, AFINN-lexicon, Opinion lexicon, Senti-Strength method, and Emotion lexicon. Feature analysis was done by conducting supervised classification for each feature sets and continued with feature selection in subjectivity and polarity domain. By using four different datasets, the results reveal that AFINN lexicon and Senti-Strength method are the best current approaches to perform Twitter Sentiment Analysis.KeywordsTwitterSentiment AnalysisComparative studyPolaritySubjectivity
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
