CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within Context

Proceedings of the 5th International Workshop on Semantic Evaluation, ACL 2010, pages 292–295, Uppsala, Sweden, 15-16 July 2010. c⃝2010 Association for Computational Linguistics CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within Context Bin LU and Benjamin K. TSOU Department of Chinese, Translation and Linguistics & Language Information Sciences Research Centre City University of Hong Kong {lubin2010, rlbtsou}@gmail.com Abstract This paper describes our system participating in task 18 of SemEval-2010, i.e. disambiguating Sentiment- Ambiguous Adjectives (SAAs). To disambiguating SAAs, we compare the machine learning-based and lexicon- based methods in our submissions: 1) Maximum entropy is used to train classifiers based on the annotated Chinese data from the NTCIR opinion analysis tasks, and the clause-level and sentence-level classifiers are compared; 2) For the lexicon-based method, we first classify the adjectives into two classes: intensifiers (i.e. adjectives intensifying the intensity of context) and suppressors (i.e. adjectives decreasing the intensity of context), and then use the polarity of context to get the SAAs’ contextual polarity based on a sentiment lexicon. The results show that the performance of maximum entropy is not quite high due to little training data; on the other hand, the lexicon-based method could improve the precision by considering the polarity of context. 1 Introduction In recent years, sentiment analysis, which mines opinions from information sources such as news, blogs, and product reviews, has drawn much attention in the NLP field (Hatzivassiloglou and McKeown, 1997; Pang et al., 2002; Turney, 2002; Hu and Liu, 2004; Pang and Lee, 2008). It has many applications such as social media monitoring, market research, and public relations. Some adjectives are neutral in sentiment polarity out of context, but they could show positive, neutral or negative meaning within specific context. Such words can be called dynamic sentiment-ambiguous adjectives (SAAs). However, SAAs have not been intentionally tackled in the researches of sentiment analysis, and usually have been discarded or ignored by most previous work. Wu et al., (2008) presents an approach of combining collocation information and SVM disambiguate SAAs, in which the collocation-to based method was first used to disambiguate adjectives within the context of collocation (i.e. a sub-sentence marked by comma), and then the SVM algorithm was explored for those instances not covered by the collocation-based method. According to their experiments, their supervised algorithm achieves encouraging performance. The task 18 at SemEval-2010 is intended to create a benchmark dataset for disambiguating SAAs. Given only 100 trial sentences, but not provided with any official training data, participants are required to tackle this problem data by unsupervised approaches or use their own training data. The task consists of 14 SAAs, which are all high-frequency words in Mandarin Chinese. They are 大|big, 小|small, 多|many, 少 |few, 高|high, 低|low, 厚|thick, 薄|thin, 深|deep, 浅|shallow, 重|heavy, 轻|light, 巨大|huge, 重大 |grave. This task deals with Chinese SAAs, but the disambiguating techniques should be language-independent. Please refer to (Wu and Jin, 2010) for more descriptions of the task. In our participating system, the annotated Chinese data from the NTCIR opinion analysis tasks is used as training data with …

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