UMBC ebiquity

Delta TFIDF: An Improved Feature Space for Sentiment Analysis

Authors: Justin Martineau, and Tim Finin

Book Title: Proceedings of the Third AAAI Internatonal Conference on Weblogs and Social Media

Date: May 17, 2009

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.

Type: InProceedings

Address: San Jose, CA

Publisher: AAAI Press

Note: (poster paper)

Tags: sentiment, natural language processing, svm, learning, natural language processing, language

Google Scholar: NrMwNfmTNykJ

Number of Google Scholar citations: 5 [show citations]

Number of downloads: 5408

 

Available for download as


size: 359933 bytes