Sentiment Mining in a Location-Based Social Networking Space: Semantically Oriented Rule-Based Reviews' Classication
Translated title
Author
Term
4. term
Education
Publication year
2011
Submitted on
2011-06-30
Pages
101
Abstract
In this work we describe a system to perform sentiment classication based on an unsupervised linguistic approach that uses natural language processing techniques to extract individual words from reviews in social network sites. Our pattern-based method applies classication rules for positive or negative sentiments depending on its overall score calculated with the aid of SentiWordNet. Searching for the best classication procedure, we investigated several classier models created from a combinations of different methods applied at word and review level; the most relevant among them has been then enhanced with additional linguistically-driven functionalities, such as spelling correction, emoticons, exclamations and negation detection. Furthermore, an empirical study on Word Sense Disambiguation has been conducted on a set of test sentences extracted from the SemCor Corpus. We dened two gloss-centered word sense disambiguation techniques which rely on overlaps and semantic relatedness calculated on disambiguated glosses' denitions provided by eXtended WordNet. Experimental results conrmed that Word Sense Disambiguation can improve sentiment classication performance; moreover, they indicated that all the words potentially carry emotions, including nouns.
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