Summary of the paper

Title Evaluating Lexical Similarity to build Sentiment Similarity
Authors Grégoire Jadi, Vincent Claveau, Béatrice Daille and 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.
Topics Lexicon, Lexical Database, Opinion Mining / Sentiment Analysis, Other
Full paper Evaluating Lexical Similarity to build Sentiment Similarity
Bibtex @InProceedings{JADI16.1215,
  author = {Grégoire Jadi and Vincent Claveau and Béatrice Daille and Laura Monceaux},
  title = {Evaluating Lexical Similarity to build Sentiment Similarity},
  booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
  year = {2016},
  month = {may},
  date = {23-28},
  location = {Portoro┼ż, Slovenia},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Sara Goggi and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {978-2-9517408-9-1},
  language = {english}
 }
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