Sentiment classification using statistical data compression models

Ziegelmayer, Dominique and Schrader, Rainer (2013) Sentiment classification using statistical data compression models.
Published In: IEEE 12th International Conference on Data Mining workshops (ICDMW 2012) : Brussels, Belgium, 10 December 2012 IEEE 2013, pp. 731-738.

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With growing availability and popularity of user generated content, the discipline of sentiment analysis has come to the attention of many researchers. Existing work has mainly focused on either knowledge based methods or standard machine learning techniques. In this paper we investigate sentiment polarity classification based on adaptive statistical data compression models. We evaluate the classification performance of the lossless compression algorithm Prediction by Partial Matching (PPM) as well as compression based measures using PPM-like character n-gram frequency statistics. Comprehensive experiments on three corpora show that compression based methods are efficient, easy to apply and can compete with the accuracy of sophisticated classifiers such as support vector machines.

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Deposit Information:
ZAIK Number: zaik2010-610
Depositing User: Dominique Ziegelmayer
Date Deposited: 19 Dec 2012 13:23
Last Modified: 23 Oct 2015 13:06

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