COMPARISON OF CLASSIFIERS FOR SENTIMENT ANALYSIS

Authors

  • Varsha D. Jadhav P.E.S.College of Engineering, Aurangabad
  • S.N. Deshmukh Dr. Babasaheb Ambedkar Marathwada University, Aurangabad

Keywords:

RELATED WORK, EXPERIMENTATION, INTRODUCTION

Abstract

The need of social media has vividly changed people’s life with more and more sharing their thoughts, expressing opinions, and in the hunt for support on social media websites such as Twitter, Facebook, blogs etc. Twitter, an online social networking and micro bloggingservice, which enables users to send and read text-based posts, known as tweets, with 140-character limit. Newspapers and blogs express opinion of news entities (people, places, things) while exposure to recent events. We present a system which extracts the sentiments from the online posts of twitter about news event. Our system shows sentiment identification, which expresses opinion associated with each entity. Also it consists of scoring phase, which assigns scores to each entity, on which the tweets are classified. Finally, we compare Maximum Entropy, Decision tree, Support vector machine and NaivesBayes classifiers.

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Published

2021-03-27

Issue

Vol. 3 No. 1 (2016): IJIERT

Section

Articles

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