Delta TFIDF: An Improved Feature Space for Sentiment Analysis

Author/Creator ORCID

Date

2009-05-17

Department

Program

Citation of Original Publication

Justin Martineau and Tim Finin , Delta TFIDF: An Improved Feature Space for Sentiment Analysis, Proceedings of the Third AAAI Internatonal Conference on Weblogs and Social Media, 2009.https://www.aaai.org/ocs/index.php/ICWSM/09/paper/view/187/504

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Abstract

Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using three well known data sets.