Paper: UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource

ACL ID S14-2137
Title UO_UA: Using Latent Semantic Analysis to Build a Domain-Dependent Sentiment Resource
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

In this paper we present our contribution to SemEval-2014 Task 4: Aspect Based Sen- timent Analysis (Pontiki et al., 2014), Sub- task 2: Aspect Term Polarity for Laptop domain. The most outstanding feature in this contribution is the automatic building of a domain-depended sentiment resource using Latent Semantic Analysis. We in- duce, for each term, two real scores that in- dicate its use in positive and negative con- texts in the domain of interest. The aspect term polarity classification is carried out in two phases: opinion words extraction and polarity classification. The opinion words related with an aspect are obtained using dependency relations. These rela- tions are provided by the Stanford Parser 1 . Finally, the polarity of the feature, in a given review, is determined from the ...