Abstract
This paper investigates the usage of various types of language models on polarity text classification – a subtask in opinion mining which deals with distinguishing between positive and negative opinions in natural language. We focus on the intrinsic benefit of different types of language models. This means that we try to find the optimal settings of a language model by examining different types of normalization, their interaction with smoothing and the benefit of class-based modeling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Salvetti, F., Reichenbach, C., Lewis, S.: Impact of Lexical Filtering on Overall Opinion Polarity Identification. In: Proc. of the AAAI Symposium on Exploring Attitude and Affect in Text: Theories & Applications, Dordrecht, Netherlands (2004)
Zhai, C., Lafferty, J.: A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval. In: Proc. of SIGIR, New Orleans, USA (2001)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification Using Machine Learning Techniques. In: Proc. of EMNLP, Philadelphia, USA (2002)
Brown, P., Pietra, V.D., de Souza, P., Lai, J., Mercer, R.: Class-Based n-gram Models of Natural Language. Computational Linguistics 18(4), 467–479 (1992)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wiegand, M., Klakow, D. (2008). Optimizing Language Models for Polarity Classification. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-540-78646-7_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78645-0
Online ISBN: 978-3-540-78646-7
eBook Packages: Computer ScienceComputer Science (R0)