Architectural elements of language engineering robustness
Natural Language Engineering
Opinion analysis for business intelligence applications
OBI '08 Proceedings of the first international workshop on Ontology-supported business intelligence
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Information Retrieval
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Computational Linguistics
Cross-Domain Contextualization of Sentiment Lexicons
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Towards linking buyers and sellers: detecting commercial Intent on twitter
Proceedings of the 22nd international conference on World Wide Web companion
Identifying purpose behind electoral tweets
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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UAHCI'13 Proceedings of the 7th international conference on Universal Access in Human-Computer Interaction: user and context diversity - Volume 2
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DUXU'13 Proceedings of the Second international conference on Design, User Experience, and Usability: web, mobile, and product design - Volume Part IV
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In this paper, we discuss a variety of issues related to opinion mining from microposts, and the challenges they impose on an NLP system, along with an example application we have developed to determine political leanings from a set of pre-election tweets. While there are a number of sentiment analysis tools available which summarise positive, negative and neutral tweets about a given keyword or topic, these tools generally produce poor results, and operate in a fairly simplistic way, using only the presence of certain positive and negative adjectives as indicators, or simple learning techniques which do not work well on short microposts. On the other hand, intelligent tools which work well on movie and customer reviews cannot be used on microposts due to their brevity and lack of context. Our methods make use of a variety of sophisticated NLP techniques in order to extract more meaningful and higher quality opinions, and incorporate extra-linguistic contextual information.