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Authors: Manuela Angioni ; Maria Laura Clemente and Franco Tuveri

Affiliation: CRS4, Center of Advanced Studies and Research and Development in Sardinia, Italy

Keyword(s): Opinion Mining, Natural Language Processing, Collaborative Filtering, Matrix Factorization, Ensemble Methods.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems ; User Profiling and Recommender Systems

Abstract: 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. (More)

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Paper citation in several formats:
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 1: ICEIS; ISBN 978-989-758-097-0; ISSN 2184-4992, SciTePress, pages 656-661. DOI: 10.5220/0005456006560661

@conference{iceis15,
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 1: ICEIS},
year={2015},
pages={656-661},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005456006560661},
isbn={978-989-758-097-0},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining
SN - 978-989-758-097-0
IS - 2184-4992
AU - Angioni, M.
AU - Laura Clemente, M.
AU - Tuveri, F.
PY - 2015
SP - 656
EP - 661
DO - 10.5220/0005456006560661
PB - SciTePress

- Science and Technology Publications, Lda.
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