Sentiment Analysis Using Machine Learning

Atul Aggarwal; Aman Raj Srivastava; Ankita Kapoor; Monis Hassan

Abstract

Addressed Twitter's sentiment analysis problem; categorized tweets based on expressed sentiment: positive, negative, or neutral. Twitter is an online microblog and social networking platform that allows users to write short status updates that are 140 characters long. It is a rapidly expanding service with more than 200 million registered users, of which 100 million are active users, and half of them log in on Twitter every daygenerating about 20,000 registered users. 250 million tweets every day. With so much usage, we want to reflect the mood of the public by analyzing the emotions expressed in tweets.

Keywords

Machine Learning, Decision Tree, Data Mining, Classification

References

[1] Tetsuya Nasukawa,Jeonghee Yi. Sentiment Analysis:capturing Favorability Using Natural Language Processing,In K-CAP '03,Florida,pages 1-8. 2003.

[2] Alekh Agarwal.Pushpak Bhattacharya, Augmenting Wordnet with polarity information on adjectives .In K-CAP 03,Florida,pages 1-8. 2003.

[3] SENTIWORDNET:A piublicly Available Lexical Resource for opinion Mining Andrea Esuli,Fabrizio Sebastini.

[4] Machine Learning,Han and Kamber,2nd edition,310-330.

Cites this article as

A. A. , A. R. Srivastava, A. K. , M. H. , "Sentiment Analysis Using Machine Learning", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-5, Issue-6, Page No-254-255, 2018. Available from: