Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 1073–1080 Manchester, August 2008 Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text Taras Zagibalov John Carroll University of Sussex Department of Informatics Brighton BN1 9QH, UK {T.Zagibalov,J.A.Carroll}@sussex.ac.uk Abstract We describe and evaluate a new method of automatic seed word selection for un- supervised sentiment classification of product reviews in Chinese. The whole method is unsupervised and does not re- quire any annotated training data; it only requires information about commonly oc- curring negations and adverbials. Unsu- pervised techniques are promising for this task since they avoid problems of do- main-dependency typically associated with supervised methods. The results ob- tained are close to those of supervised classifiers and sometimes better, up to an F1 of 92%. 1 Introduction Automatic classification of document sentiment (and more generally extraction of opinion from text) has recently attracted a lot of interest. One of the main reasons for this is the importance of such information to companies, other organizations, and individuals. Applications include marketing research tools that help a company see market or media reaction towards their brands, products or services, or search engines that help potential purchasers make an informed choice of a product they want to buy. Sentiment classification research has drawn on and contributed to research in text classification, unsupervised machine learning, and cross- domain adaptation. This paper presents a new, automatic approach to automatic seed word selection as part of senti- ment classification of product reviews written in Chinese, which addresses the problem of do- © 2008. Licensed under the Creative Commons Attribu- tion-Noncommercial-Share Alike 3.0 Unported license (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. main-dependency of sentiment classification that has been observed in previous work. It may also facilitate building sentiment classification sys- tems in other languages since the approach as- sumes a very small amount of linguistic knowl- edge: the only language-specific information re- quired is a basic description of the most frequent negated adverbial constructions in the language. The paper is structured as follows. Section 2 surveys related work in sentiment classification, unsupervised machine learning and Chinese language processing. Section 3 motivates our approach, which is described in detail in Section 4. The data used for experiments and baselines, as well as the results of experiments are covered in Section 5. Section 6 discusses the lessons learned and proposes directions for future work. 2 Related Work 2.1 Sentiment Classification Most work on sentiment classification has used approaches based on supervised machine learn- ing. For example, Pang et al. (2002) collected movie reviews that had been annotated with re- spect to sentiment by the authors of the reviews, and used this data to train supervised classifiers. A number of studies have investigated the impact on classification accuracy of different factors, in- cluding choice of feature set, machine learning algorithm, and pre-selection of the segments of text to be classified. For example, Dave et al. (2003) experiment with the use of linguistic, sta- tistical and n-gram features and …

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