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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Cambria, Erik
Speer, Robert
Havasi, Catherine
Hussain, Amir
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Title: SenticNet: A publicly available semantic resource for opinion mining
Citation: Cambria E, Speer R, Havasi C & Hussain A (2010) SenticNet: A publicly available semantic resource for opinion mining. In: Commonsense Knowledge: Papers from the AAAI Fall Symposium. Fall Symposium Series Technical Reports, FS-10-02. 2010 AAAI Fall Symposium, Arlington, VA, USA, 11.11.2010-13.11.2010. Menlo Park, CA, USA: AAAI Press, pp. 14-18.
Issue Date: 2010
Date Deposited: 20-Dec-2017
Series/Report no.: Fall Symposium Series Technical Reports, FS-10-02
Conference Name: 2010 AAAI Fall Symposium
Conference Dates: 2010-11-11 - 2010-11-13
Conference Location: Arlington, VA, USA
Abstract: Today millions of web-users express their opinions about many topics through blogs, wikis, fora, chats and social networks. For sectors such as e-commerce and e-tourism, it is very useful to automatically analyze the huge amount of social information available on the Web, but the extremely unstructured nature of these contents makes it a difficult task. SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques. It uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level.
Status: VoR - Version of Record
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