Sentiment Classification Considering Negation and Contrast Transition

Sentiment Classification Considering Negation and Contrast Transition ∗∗∗∗ Shoushan Li and Chu-Ren Huang Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University {shoushan.li, churenhuang}@gmail.com Abstract. Negation and contrast transition are two kinds of linguistic phenomena which are popularly used to reverse the sentiment polarity of some words and sentences. In this paper, we propose an approach to incorporate their classification information into our sentiment classification system: First, we classify sentences into sentiment reversed and non-reversed parts. Then, represent them as two different bags-of-words. Third, present three general strategies to do classification with two-bag-of-words modeling. We collect a large-scale product reviews involving five domains and conduct our experiments on them. The experimental results show that incorporating both negation and contrast transition information is effective and performs robustly better than traditional machine learning approach (based on one-bag-of-words modeling) across five different domains. Keywords: Sentiment classification, opinion mining, linear Classifier. Copyright 2009 by Shoushan Li and Chu-Ren Huang 1 Introduction Sentiment classification is a task to classify text according to sentimental polarities of opinions they contain (e.g., favorable or unfavorable). This task has received considerable interests in computational linguistic community due to its wide applications. In the latest studies of this task, machine learning techniques become the state-of-the-art approach and have achieved much better results than some rule-based approaches (Kennedy and Inkpen, 2006; Pang et al., 2002) . In machine learning approach, a document (text) is usually modeled as a bag-of-words, a set of words without any word order or syntactic relation information. Therefore, the whole sentimental orientation is highly influenced by the sentiment polarity of each word. Notice that although each word takes a fixed sentiment polarity itself, its polarity contributed to the whole sentence or document might be completely the opposite. Negation and contrast transition are exactly the two kinds of linguistic phenomena which are able to reverse the sentiment polarity. For example, see a sentence containing negation "this movie is not good" and another sentence containing contrast transition "this mouse is good looking, but it works terribly". The sentiment polarity of the word good in these two sentences is positive but the whole sentences are negative. Therefore, we can see that the whole sentiment is not necessarily the sum of the parts (Turney, 2002). This phenomenon is one main reason why machine learning often fails to classify some testing samples (Dredze et al., 2008). Fortunately, a language usually has some special words which indicate the possible polarity shift of a word or even a sentence. These words are called contextual valence shifters (CVSs) which can cause the valence of a lexical item to shift from one pole to the other or, less forcefully, even to modify the valence towards a more neutral position (Polanyi and Zaenen, 2006). Generally speaking, CVSs are classified into two categories: sentence-based and 297 23rd Pacific Asia Conference on Language, Information and Computation, pages 297–306 discourse-based (Polanyi and Zaenen, 2006). Sentence-based CVSs are responsible for shifting valence of some words in a sentence. The …

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