EVALUATING POTENTIAL IMPROVEMENTS OF COLLABORATIVE FILTERING WITH OPINION MINING

An integration of an Opinion Mining approach with a Collaborative Filtering algorithm has been applied to the Yelp dataset to improve the predictions through the information provided by the user-generated textual reviews. The research, still in progress, based the Opinion Mining approach on the syntactic analysis of textual reviews and on a beginning polarity evaluation of the sentences. The predictions produced in this way was blended with the predictions coming from a Biased Matrix Factorization algorithm obtaining interesting results in terms of Root Mean Squared Error (RMSE), with potential enhancements. We intend to improve these results in a further phase of activity by including in the Opinion Mining approach the semantic disambiguation and by using better criteria of evaluation of the reviews taking into account a set of 12 business aspects. The Opinion Mining approach will be evaluated comparing the output in terms of predictions with the values manually assigned by a small group of people to a sample of the same reviews.

@InProceedings{ACT15,
  author       = {Angioni, M. and Clemente, M. and Tuveri, F.},
  title        = {EVALUATING POTENTIAL IMPROVEMENTS OF COLLABORATIVE FILTERING WITH OPINION MINING},
  booktitle    = {ICEIS 2015 - Proceeedings of the 17th International Conference on Enterprise  Information Systems},
  year         = {2015},
  publisher    = {Scitepress},
  keywords     = {Opinion Mining, Natural Language Processing, Collaborative Filtering, Matrix Factorization, Ensemble methods},
  url          = {https://publications.crs4.it/pubdocs/2015/ACT15},
}