Sentiment Quantifi cation
نویسنده
چکیده
These articles present unique innovative research, computational methods, and selected results and examples. In “Sentiment Quantifi cation,” Andrea Esuli and Fabrizio Sebastiani argue that the opinionmining community has traditionally neglected whether the analysis of large quantities of text should be carried out at the individual or aggregate level. They review several sentimentquantifi cation methods that can help address some of these issues. In the third and last paper, “Intelligent Feature Selection for Opinion Classifi cation,” Ahmed Abbasi proposes an intelligent feature-selection approach inspired by feature subsumption hierarchies (FSH) that incorporates syntactic and semantic information. The proposed approach illustrates how rich, heterogeneous feature sets, coupled with appropriate feature-selection mechanisms, can improve opinion classifi cation performance.
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