Employing Personal/Impersonal Views in Supervised and Semi-Supervised Sentiment Classification

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 414–423, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Employing Personal/Impersonal Views in Supervised and Semi-supervised Sentiment Classification Shoushan Li†‡ Chu-Ren Huang† Guodong Zhou‡ Sophia Yat Mei Lee† †Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University {shoushan.li,churenhuang, sophiaym}@gmail.com ‡ Natural Language Processing Lab School of Computer Science and Technology Soochow University, China gdzhou@suda.edu.cn Abstract In this paper, we adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised sentiment classification. Here, personal views consist of those sentences which directly express speaker’s feeling and preference towards a target object while impersonal views focus on statements towards a target object for evaluation. To obtain them, an unsupervised mining approach is proposed. On this basis, an ensemble method and a co-training algorithm are explored to employ the two views in supervised and semi-supervised sentiment classification respectively. Experimental results across eight domains demonstrate the effectiveness of our proposed approach. 1 Introduction As a special task of text classification, sentiment classification aims to classify a text according to the expressed sentimental polarities of opinions such as ‘thumb up’ or ‘thumb down’ on the movies (Pang et al., 2002). This task has recently received considerable interests in the Natural Language Processing (NLP) community due to its wide applications. In general, the objective of sentiment classification can be represented as a kind of binary relation R, defined as an ordered triple (X, Y, G), where X is an object set including different kinds of people (e.g. writers, reviewers, or users), Y is another object set including the target objects (e.g. products, events, or even some people), and G is a subset of the Cartesian product X ×Y . The concerned relation in sentiment classification is X ’s evaluation on Y, such as ‘thumb up’, ‘thumb down’, ‘favorable’, and ‘unfavorable’. Such relation is usually expressed in text by stating the information involving either a person (one element in X ) or a target object itself (one element in Y ). The first type of statement called personal view, e.g. ‘I am so happy with this book’, contains X ’s “subjective” feeling and preference towards a target object, which directly expresses sentimental evaluation. This kind of information is normally domain-independent and serves as highly relevant clues to sentiment classification. The latter type of statement called impersonal view, e.g. ‘it is too small’, contains Y ’s “objective” (i.e. or at least criteria-based) evaluation of the target object. This kind of information tends to contain much domain-specific classification knowledge. Although such information is sometimes not as explicit as personal views in classifying the sentiment of a text, speaker’s sentiment is usually implied by the evaluation result. It is well-known that sentiment classification is very domain-specific (Blitzer et al., 2007), so it is critical to eliminate its dependence on a large-scale labeled data for its wide applications. Since the unlabeled data is ample and easy to collect, a …

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