Combining Opinion Mining with Collaborative Filtering

Manuela Angioni, Maria Laura Clemente, Franco Tuveri


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).


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Paper Citation

in Harvard Style

Angioni M., Clemente M. and Tuveri F. (2015). Combining Opinion Mining with Collaborative Filtering . In Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-106-9, pages 370-380. DOI: 10.5220/0005412403700380

in Bibtex Style

author={Manuela Angioni and Maria Laura Clemente and Franco Tuveri},
title={Combining Opinion Mining with Collaborative Filtering},
booktitle={Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},

in EndNote Style

JO - Proceedings of the 11th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Combining Opinion Mining with Collaborative Filtering
SN - 978-989-758-106-9
AU - Angioni M.
AU - Clemente M.
AU - Tuveri F.
PY - 2015
SP - 370
EP - 380
DO - 10.5220/0005412403700380