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

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Abstract

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.

Item Type:	Book Section, Proceedings Item or annotation in a legal commentary
Creators:	Creators Email ORCID ORCID Put Code Ziegelmayer, Dominique UNSPECIFIED UNSPECIFIED UNSPECIFIED Schrader, Rainer UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-550361
Title of Book:	IEEE 12th International Conference on Data Mining workshops (ICDMW 2012)
Page Range:	pp. 731-738
Date:	2013
Publisher:	IEEE
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Mathematics and Computer Science > Institute of Computer Science
Subjects:	Data processing Computer science
Uncontrolled Keywords:	Keywords Language ARRAY(0x55e9334e5430) UNSPECIFIED
Refereed:	No
URI:	http://kups.ub.uni-koeln.de/id/eprint/55036