Delta TFIDF: An Improved Feature Space for Sentiment Analysis

Authors

  • Justin Martineau University of Maryland Baltimore County
  • Tim Finin University of Maryland Baltimore County

Keywords:

Sentiment Analysis, Feature Weighting, SVM, Bag of Words

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.

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Published

2009-03-20

How to Cite

Martineau, J., & Finin, T. (2009). Delta TFIDF: An Improved Feature Space for Sentiment Analysis. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 258-261. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/13979