Commonsense Knowledge: Papers from the AAAI Fall Symposium (FS-10-02) SenticNet: A Publicly Available Semantic Resource for Opinion Mining Erik Cambria Robyn Speer Catherine Havasi Amir Hussain University of Stirling MIT Media Lab MIT Media Lab University of Stirling Stirling, FK4 9LA UK Cambridge, 02139-4307 USA Cambridge, 02139-4307 USA Stirling, FK4 9LA UK
[email protected] [email protected] [email protected] [email protected] Abstract opinions and attitudes in natural language texts. In particu- lar, given a textual resource containing a number of opinions Today millions of web-users express their opinions o about a number of topics, we would like to be able to as- about many topics through blogs, wikis, fora, chats p(o) ∈ [−1, 1] and social networks. For sectors such as e-commerce sign each opinion a polarity , representing and e-tourism, it is very useful to automatically ana- a range from generally unfavorable to generally favorable, lyze the huge amount of social information available on and to aggregate the polarities of opinions on various topics the Web, but the extremely unstructured nature of these to discover the general sentiment about those topics. contents makes it a difficult task. SenticNet is a publicly Existing approaches to automatic identification and ex- available resource for opinion mining built exploiting traction of opinions from text can be grouped into three main AI and Semantic Web techniques. It uses dimension- categories: keyword spotting, in which text is classified into ality reduction to infer the polarity of common sense categories based on the presence of fairly unambiguous af- concepts and hence provide a public resource for min- fect words (Ortony, Clore, and Collins 1998)(Wiebe, Wil- ing opinions from natural language text at a semantic, son, and Claire 2005), lexical affinity, which assigns ar- rather than just syntactic, level.