Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions -- this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector Machines) where the main features...

• Type: Journal
• Year: 2009
• Where: ICWSM
• Authors: Jason S. Kessler, Nicolas Nicolov
• Keywords: ICWSM 2009, Internet Technology, Search Query, Sentiment Expression, Sentiment Propagation Graph