Evaluating Lexical Similarity to build Sentiment Similarity

Grégoire Jadi (1) , Vincent Claveau (2) , Béatrice Daille (1) , Laura Monceaux-Cachard (1)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments

In this article, we propose to evaluate the lexical similarity information provided by word representations against several opinion resources using traditional Information Retrieval tools. Word representation have been used to build and to extend opinion resources such as lexicon, and ontology and their performance have been evaluated on sentiment analysis tasks. We question this method by measuring the correlation between the sentiment proximity provided by opinion resources and the semantic similarity provided by word representations using different correlation coefficients. We also compare the neighbors found in word representations and list of similar opinion words. Our results show that the proximity of words in state-of-the-art word representations is not very effective to build sentiment similarity.

Keywords: opinion lexicon evaluation; sentiment analysis; spectral representation; word embedding

Domains: Artificial Intelligence [cs.AI]; Computation and Language [cs.CL]; Information Retrieval [cs.IR]

Grégoire Jadi, Vincent Claveau, Béatrice Daille, Laura Monceaux-Cachard. Evaluating Lexical Similarity to build Sentiment Similarity. Language and Resource Conference, LREC, May 2016, portoroz, Slovenia. ⟨hal-01394768⟩