Combining Opinion Mining with Collaborative Filtering

Manuela Angioni, Maria Laura Clemente, Franco Tuveri

Abstract

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).

References

  1. Agerri, R., Garcia-Serrano A., 2010. Q-WordNet: Extracting polarity from WordNet senses. In LREC 2010, 7th International Conference on Language Resources and Evaluation, Malta.
  2. Baccianella, S., Esuli, A., Sebastiani, F., 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In LREC 2010, 7th International Conference on Language Resources and Evaluation, Malta, pp. 2200-2204.
  3. Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Venkatramana S. Subrahmanian, 2007. Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone. Proceedings of ICWSM 07, International Conference on Weblogs and Social Media, pp. 203-206.
  4. Burke, R., 2002. Hybrid Recommender Systems: Survey and Experiments, User Modeling and User-Adapted Interaction, vol. 12, n. 3, pp. 331-370.
  5. Clark, P., 2013. Yelp's Newest Weapon Against Fake Reviews: Lawsuits, http://www.businessweek.com/ articles/2013-09-09/yelps-newest-weapon-againstfake-reviews-lawsuits.
  6. Clemente, M. L., 2008. Experimental Results on ItemBased Algorithms for Independent Domain Collaborative Filtering, Proceedings of AXMEDIS 7808, IEEE Computer Society, pp. 87-92.
  7. Ding, X., Liu, B., Yu, P.S., 2008. A Holistic LexiconBased Approach to Opinion Mining. WSDM 7808 Proceedings of the international conference on Web search and web data mining, ACM New York, NY, USA.
  8. Ganu, G., Elhadad, N., Marian, A., 2009. Beyond the Stars: Improving Rating Predictions using Review Text Content, Twelfth International Workshop on the Web and Databases (WebDB 2009), Providence, Rhode Island, USA.
  9. Ganu, G., Kakodkar, Y., Marian, A., 2012, Improving the quality of predictions using textual information in online user reviews”, Information Systems, http://dx.doi.org/10.1016/j.is.2012.03.001.
  10. Ghose, A., Ipeirotis, P. G., 2007. Designing novel review ranking systems: Pre- dicting usefulness and impact of reviews, in Proceedings of the International Conference on Electronic Commerce (ICEC).
  11. Govindarajan, M., 2014, Sentiment Analysis of Restaurant Reviews Using Hybrid Classification Method, International Journal of Soft Computing and Artificial Intelligence, Vol. 2, Issue 1.
  12. Hinton, G., 2012, A Practical Guide to Training Restricted Boltzmann Machines, Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science Volume 7700, pp 599-619.
  13. Huang, J., Rogers, S., Joo, E., 2014. Improving Restaurants by Extracting Subtopics from Yelp Reviews, SOCIAL MEDIA EXPO, https://www. ideals.illinois.edu/bitstream/handle/2142/48832/Huang -iConference2014-SocialMediaExpo.pdf ?sequence=2.
  14. Jahrer, M., Töscher, A., Legenstein, R., 2010. Combining Predictions for Accurate Recommender Systems, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 693-702, ACM, 2010.
  15. Jong, J., 2011. Predicting Rating with Sentiment Analysis http://cs229.stanford.edu/proj2011/Jong%20PredictingRatingwithSentimentAnalysis.pdf.
  16. Koren, Y., 2009. The bellkor solution to the netflix granprize, http://www.netflixprize.com/assets/Grand Prize2009_BPC_BellKor.pdf.
  17. Koren, Y., Bell, R., Volinsky, C., 2009. Matrix Factorization Techniques for Recommender Systems, Computer, IEEE Computer Society, v. 42, n. 8.
  18. Koukourikos, A., Stoisis, G., Karampiperis, P., 2012. Sentiment Analysis: A tool for Rating Attribution to Content in Recommender Systems. Presented at the 2nd Workshop on Recommender Systems for Technology Enhances Learning (RecSysTEL 2012), 18-19/09/2012, Saarbrucken, Germany.
  19. Lee, D., Jeong, O.R., Lee, S., 2008. Opinion Mining of customer feedback data on the web. In ICUIMC 7808 Proceedings of the 2nd international conference on Ubiquitous information management communication.
  20. Levi, A., Mokryn, O., Diot, C., Taft, N., 2012. Find-ing a needle in a haystack of reviews: cold start contextbased hotel recommender system. In Proceedings of the sixth ACM conference on Recommender systems, pages 115-122. ACM, 2012.
  21. Linden, G., Smith, B., York, J., 2003. Amazon.com Recommendations, IEEE Internet Computing, vol. 07, n. 1, pp. 76-80.
  22. Miller, G., 1998. WordNet: An Electronic Lexical Database, Bradford Books.
  23. Mingming, F., Khademi, Maryam, 2014. Predicting a Business Star in Yelp from Its Reviews Text Alone. ArXiv e-prints: 1401.0864.
  24. Owen, S., Anil, R., Dunning, T., Friedman E., 2011. Mahout in Action, Manning Publications Co., ISBN: 9781935182689.
  25. Pang B., Lee L., 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), pp. 1-135. DOI: 10.1561/1500000011.
  26. Paterek, A., 2007. Improving regularized singular value decomposition for collaborative filtering, Proc. KDDCup and Workshop, ACM Press, pp. 39-42.
  27. Quadrana, M., 2013. E-tourism recommender systems http://hdl.handle.net/10589/84901.
  28. Sarwar, B., Karypis, G., Konstan, J., J. Riedl, J., 2001. Item-Based Collaborative Filtering Recommendation Algorithms, in Proc. IEEE Internet Computing, 10th International World Wide Web Conference.
  29. Shelter, S. & Owen, S., 2012, Collaborative Filtering with Apache Mahout, RecSysChallenge'12.
  30. Schmid, H., 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees. In Proceedings of the International Conference on New Methods in Language Processing, pp. 44-49.
  31. Singh, V. K., Mukherjee, M., Mehta, G. K., 2011. Combining Collaborative Filtering and Sentiment Classification for Improved Movie Recommendations. In Sombattheera et al. (Eds.): Multi-disciplinary Thrends in Artificial Intelligence, LNAI 7080, Springer-Verlag, Berlin Heidelberg, pp. 38-50.
  32. Tosher, A., Jahrer, M., Bell, R. M., 2009. The BigChaos solution to the Netflix grand prize, Netflix Prize Documentation.
  33. Trevisiol, M., Chiarandini, L., Baeza-Yates, R., 2014. Buon Appetito - Recommending Personalized menus.
  34. Tuveri, F., Angioni, M., 2012. A Linguistic Approach to Feature Extraction Based on a Lexical Database of the Properties of Adjectives and Adverbs, Global WordNet Conference (GWC2012), Matsue, Japan.
  35. Wu, Y., Ester, M., 2015, FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering, WSDM'2015, Shanghai, China.
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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

@conference{webist15,
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,},
year={2015},
pages={370-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005412403700380},
isbn={978-989-758-106-9},
}


in EndNote Style

TY - CONF
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