document: Automatic construction of a context-aware sentiment lexicon : an optimization approach The explosion of Web opinion data has made essential the need for automatic tools to analyze and understand people 's sentiments toward different topics . In most sentiment analysis applications , the sentiment lexicon plays a central role . However , it is well known that there is no universally optimal sentiment lexicon since the polarity of words is sensitive to the topic domain . Even worse , in the same domain the same word may indicate different polarities with respect to different aspects . For example , in a laptop review , `` large '' is negative for the battery aspect while being positive for the screen aspect . In this paper , we focus on the problem of learning a sentiment lexicon that is not only domain specific but also dependent on the aspect in context given an unlabeled opinionated text collection . We propose a novel optimization framework that provides a unified and principled way to combine different sources of information for learning such a context-dependent sentiment lexicon . Experiments on two data sets ( hotel reviews and customer feedback surveys on printers ) show that our approach can not only identify new sentiment words specific to the given domain but also determine the different polarities of a word depending on the aspect in context . In further quantitative evaluation , our method is proved to be effective in constructing a high quality lexicon by comparing with a human annotated gold standard . In addition , using the learned context-dependent sentiment lexicon improved the accuracy in an aspect-level sentiment classification task .
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