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International Conference on Web Information Systems and Technologies

WEBIST 2010: Web Information Systems and Technologies pp 202–214Cite as

An Empirical Study on Machine Learning-Based Sentiment Classification Using Polarity Clues

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 75))

Abstract

In recent years a variety of approaches in classifying the sentiment polarity of texts have been proposed. While in the majority of approaches the determination of subjectivity or polarity-related term features is at the center, the number of publicly available dictionaries is rather limited. In this paper, we investigate the performance of combining lexical resources with machine learning based classifier for the task of sentiment classification.We systematically analyze four different English and three different German polarity dictionaries as a resources for a sentiment-based feature selection. The evaluation results show that smaller but more controlled dictionaries used for feature selection perform within a SVM-based classification setup equally good compared to the biggest available resources.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Group, Cognitive Interaction Technology Excellence Center (CITEC), Bielefeld University, Bielefeld, Germany

    Ulli Waltinger

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  1. Ulli Waltinger
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Editor information

Editors and Affiliations

  1. Department of Systems and Informatics, Polytechnic Institute of Setúbal, Rua do Vale de Chaves - Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe

  2. Institute for Systems and Technologies of Information, Control and Communication Department of Systems and Informatics, Polytechnic Institute of Setúbal, Rua do Vale de Chaves - Estefanilha, 2910-761, Setúbal, Portugal

    José Cordeiro

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Waltinger, U. (2011). An Empirical Study on Machine Learning-Based Sentiment Classification Using Polarity Clues. In: Filipe, J., Cordeiro, J. (eds) Web Information Systems and Technologies. WEBIST 2010. Lecture Notes in Business Information Processing, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22810-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-22810-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22809-4

  • Online ISBN: 978-3-642-22810-0

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