SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives
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SemEval-2010 task 18: disambiguating sentiment ambiguous adjectives Yunfang Wu · Peng Jin
Published online: 1 December 2012 © Springer Science+Business Media Dordrecht 2012
Abstract Sentiment ambiguous adjectives, which have been neglected by most previous researches, pose a challenging task in sentiment analysis. We present an evaluation task at SemEval-2010, designed to provide a framework for comparing different approaches on this problem. The task focuses on 14 Chinese sentiment ambiguous adjectives, and provides manually labeled test data. There are 8 teams submitting 16 systems in this task. In this paper, we define the task, describe the data creation, list the participating systems, and discuss different approaches. Keywords Sentiment ambiguous adjectives · Sentiment analysis · Word sense disambiguation · SemEval
1 Introduction In recent years, sentiment analysis has attracted considerable attention in the field of natural language processing. It is the task of mining positive and negative opinions from real texts, which can be applied to many natural language application systems, such as document summarization and question answering. Previous work on this problem falls into three groups: opinion mining of documents, sentiment classification of sentences and polarity prediction of words. Sentiment analysis at both document and sentence level relies heavily on word level. Another line of Y. Wu (&) Key Laboratory of Computational Linguistics (Peking University), Ministry of Education, Beijing, China e-mail: [email protected] P. Jin Laboratory of Intelligent Information Processing and Application, Leshan Normal University, Leshan, China e-mail: [email protected]
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research is feature-based sentiment analysis that extracts product features and the opinion towards them (e.g. Jin and Ho 2009; Li et al. 2010), which is also based on the lexical semantic orientation. The most frequently explored task at word level is to determine the semantic orientation (SO) of words, in which most work centers on assigning a prior polarity to words or word senses in the lexicon out of context. However, for some words, the polarity varies strongly with context. For instance, the word “low” has a positive orientation in “low cost” but a negative orientation in “low salary”. This makes it hard to attach each word to a specific sentiment category in the lexicon. Turney and Littman (2003) claim that sentiment ambiguous words cannot be avoided easily in a real-world application. But unfortunately, sentiment ambiguous words are neglected by most researches concerning sentiment analysis (e.g., Hatzivassiloglou and McKeown 1997; Turney and Littman 2003; Kim and Hovy 2004). Also, sentiment ambiguous words have not been intentionally tackled in the researches of word sense disambiguation, where senses are defined as word meanings rather than semantic orientations. Actually, disambiguating sentiment ambiguous words is an interaction task between sentiment analysis and word sense disambiguation. Our task at SemEval-20
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