HITSZ_CITYU: Combine Collocation, Context Words and Neighboring Sentence Sentiment in Sentiment Adjectives Disambiguation

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 448–451, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics HITSZ_CITYU: Combine Collocation, Context Words and Neighbor- ing Sentence Sentiment in Sentiment Adjectives Disambiguation Ruifeng Xu1,2, Jun Xu1 1Harbin Institute of Technology, Shenzhen Campus, China xuruifeng@hitsz.edu.cn hit.xujun@gmail.com Chunyu Kit2 2City University of Hong Kong, Hong Kong ctckit@cityu.edu.hk Abstract This paper presents the HIT_CITYU systems in Semeval-2 Task 18, namely, disambiguat- ing sentiment ambiguous adjectives. The base- line system (HITSZ_CITYU_3) incorporates bi-gram and n-gram collocations of sentiment adjectives, and other context words as features in a one-class Support Vector Machine (SVM) classifier. To enhance the baseline system, col- location set expansion and characteristics learning based on word similarity and semi- supervised learning are investigated, respec- tively. The final system (HITSZ_CITYU_1/2) combines collocations, context words and neighboring sentence sentiment in a two-class SVM classifier to determine the polarity of sentiment adjectives. The final systems achieved 0.957 and 0.953 (ranked 1st and 2nd) macro accuracy, and 0.936 and 0.933 (ranked 2nd and 3rd) micro accuracy, respectively. 1 Introduction Sentiment analysis is always puzzled by the con- text-dependent sentiment words that one word brings positive, neutral or negative meanings in different contexts. Hatzivassiloglou and Mckeown (1997) predicated the polarity of ad- jectives by using the pairs of adjectives linked by consecutive or negation conjunctions. Turney and Littman (2003) determined the polarity of sentiment words by estimating the point-wise mutual information between sentiment words and a set of seed words with strong polarity. An- dreevskaia and Bergler (2006) used a Sentiment Tag Extraction Program to extract sentiment- bearing adjectives from WordNet. Esuli and Se- basian (2006) studied the context-dependent sen- timent words in WordNet but ignored the in- stances in real context. Wu et al. (2008) applied collocation plus a SVM classifier in Chinese sen- timent adjectives disambiguation. Xu et al. (2008) proposed a semi-supervised learning algorithm to learn new sentiment word and their context- dependent characteristics. Semeval-2 Task 18 is designed to provide a common framework and dataset for evaluating the disambiguation techniques for Chinese sen- timent adjectives. The HITSZ_CITYU group submitted three runs corresponding to one base- line system and one improved systems (two runs). The baseline system (HITSZ_CITYU_3) is based on collocations between sentiment words and their targets as well as their context words. For the ambiguous adjectives, 412 positive and 191 negative collocations are built from a 100- million-word corpus as the seed collocation set. Using the context words of seed collocations as features, a one-class SVM classifier is trained in the baseline system. Using HowNet-based word similarity as clue, the seed collocations are ex- panded to improve the coverage of collocation- based technique. Furthermore, a semi-supervised learning algorithm is developed to learn new col- locations between sentiment words and their tar- gets from raw corpus. Finally, the inner sentence features, such as collocations and context words, and the inter sentence features, i.e. neighboring sentence sentiments, are incorporated to deter- mine the polarity of ambiguous adjectives. The improved …

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