Evaluating Lexical Similarity to build Sentiment Similarity

Grégoire Jadi, Vincent Claveau, Béatrice Daille, Laura Monceaux


Abstract
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.
Anthology ID:
L16-1190
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1196–1201
Language:
URL:
https://aclanthology.org/L16-1190
DOI:
Bibkey:
Cite (ACL):
Grégoire Jadi, Vincent Claveau, Béatrice Daille, and Laura Monceaux. 2016. Evaluating Lexical Similarity to build Sentiment Similarity. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 1196–1201, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Evaluating Lexical Similarity to build Sentiment Similarity (Jadi et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1190.pdf