Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14993
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dc.contributor.authorAnees A.A.
dc.contributor.authorPrakash Gupta H.
dc.contributor.authorDalvi A.P.
dc.contributor.authorGopinath S.
dc.contributor.authorMohan B.R.
dc.date.accessioned2021-05-05T10:16:09Z-
dc.date.available2021-05-05T10:16:09Z-
dc.date.issued2019
dc.identifier.citation2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019 , Vol. , , p. 637 - 641en_US
dc.identifier.urihttps://doi.org/10.1109/ICCS45141.2019.9065895
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14993-
dc.description.abstractInformation sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X2 statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme. © 2019 IEEE.en_US
dc.titlePerformance analysis of multiple classifiers using different term weighting schemes for sentiment analysisen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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