Please use this identifier to cite or link to this item: https://doi.org/10.1145/2124295.2124319
Title: Mining slang and urban opinion words and phrases from cQA services: An optimization approach
Authors: Amiri, H.
Chua, T.-S. 
Keywords: Opinion lexicon
Opinion mining
Sentiment analysis
Sentiment orientation
Slang
Urban word
Issue Date: 2012
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
URI: http://scholarbank.nus.edu.sg/handle/10635/40609
ISBN: 9781450307475
DOI: 10.1145/2124295.2124319
Appears in Collections:Staff Publications

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