Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining

Manuela Angioni, Maria Laura Clemente, Franco Tuveri


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.


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Paper Citation

in Harvard Style

Angioni M., Laura Clemente M. and Tuveri F. (2015). Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining . In Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-097-0, pages 656-661. DOI: 10.5220/0005456006560661

in Bibtex Style

author={Manuela Angioni and Maria Laura Clemente and Franco Tuveri},
title={Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining},
booktitle={Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining
SN - 978-989-758-097-0
AU - Angioni M.
AU - Laura Clemente M.
AU - Tuveri F.
PY - 2015
SP - 656
EP - 661
DO - 10.5220/0005456006560661