COMBINING OPINION MINING WITH COLLABORATIVE FILTERING

An experimental analysis of a combination of Opinion Mining and Collaborative Filtering algorithms is presented. The analysis used the Yelp dataset in order to have both the textual reviews and the star ratings provided by the users. The Opinion Mining algorithm was used to work on the textual reviews, while the Collaborative Filtering worked on the star ratings. The research activity carried out shows that most of the Yelp users provided star ratings corresponding to the related textual review, but in many cases an inconsistence was evident. A set of thresholds and coefficients were applied in order to test a hypothesis about the influence of restaurant popularity on the user ratings. Interesting results have been obtained in terms of Root Mean Squared Error (RMSE).

@InProceedings{ACT15a,
  author       = {Angioni, M. and Clemente, M. and Tuveri, F.},
  title        = {COMBINING OPINION MINING WITH COLLABORATIVE FILTERING},
  booktitle    = {WEBIST 2015 - 11th International Conference on Web Information Systems and Technologies},
  year         = {2015},
  keywords     = {Opinion Mining, Natural Language Processing, Collaborative Filtering, Matrix Factorization, Ensemble methods},
  url          = {https://publications.crs4.it/pubdocs/2015/ACT15a},
}