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|Title:||Mining slang and urban opinion words and phrases from cQA services: An optimization approach|
|Source:||Amiri, H.,Chua, T.-S. (2012). Mining slang and urban opinion words and phrases from cQA services: An optimization approach. WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining : 193-202. ScholarBank@NUS Repository. https://doi.org/10.1145/2124295.2124319|
|Abstract:||Current opinion lexicons contain most of the common opinion words, but they miss slang and so-called urban opinion words and phrases (e.g. delish, cozy, yummy, nerdy, and yuck). These subjectivity clues are frequently used in community questions and are useful for opinion question analysis. This paper introduces a principled approach to constructing an opinion lexicon for community-based question answering (cQA) services. We formulate the opinion lexicon induction as a semi-supervised learning task in the graph context. Our method makes use of existing opinion words to extract new opinion entities (slang and urban words/phrases) from community questions. It then models the opinion entities in a graph context to learn the polarity of the new opinion entities based on the graph connectivity information. In contrast to previous approaches, our method not only learns such polarities from the labeled data but also from the unlabeled data and is more feasible in the web context where the dictionary-based relations (such as synonym, antonym, or hyponym) between most words are not available for constructing a high quality graph. The experiments show that our approach is effective both in terms of the quality of the discovered new opinion entities as well as its ability in inferring their polarity. Furthermore, since the value of opinion lexicons lies in their usefulness in applications, we show the utility of the constructed lexicon in the sentiment classification task. Copyright 2012 ACM.|
|Source Title:||WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining|
|Appears in Collections:||Staff Publications|
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