Abstract
In this paper information extraction method for the restaurant recommendation system is proposed. We aim at the development of an information extraction (IE) system which is intended to be a module of the recommendation system. The IE system is to gather information about different aspects of restaurants from online reviews, structure it and feed the recommendation module with the obtained data. The analyzed frames include service and food quality, cuisine, price level, noise level, etc. In this paper service quality, cuisine type and food quality are considered. As part of corpus preprocessing phase, a method for Russian reviews corpus analysis (as part of information extraction) is proposed. Its importance is shown at the experimental phase, when the application of machine learning techniques to aspects extraction is analyzed. It is shown that the ideas obtained at the corpus preprocessing stage can help to improve machine learning models performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Almazro, D., Shahatah, G., Albdulkarim, L., Kherees, M., Martinez, R., Nzoukou, W.: A Survey Paper on Recommender Systems. Arxiv preprint, arXiv:1006.5278 (2010)
Bakliwal, A., Patil., A., Arora, P., Varma, V.: Towards Enhanced Opinion Classification using NLP Techniques. In: Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP), IJCNLP, Chiang Mai, Thailand, November 13, pp. 101–107 (2011)
Benamara, F., Cesarano, C., Picariello, A., Reforgiato, D., Subrahmanian, V.S.: Sentiment analysis: Adjectives and adverbs are better than adjectives alone. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM) (2007)
Bermingham, A., Smeaton, A.: Classifying Sentiment in Microblogs: Is Brevity an Advantage? In: CIKM 2010, Toronto, Ontario, Canada, October 26-29 (2010)
Bodapati, A.V.: Recommendation Systems with Purchase Data. Journal of Marketing Research 45(1), 77–93 (2008)
Carlson, A., Betteridge, J., Wang, R.C.: Coupled Semi-Supervised Learning for Information Extraction. In: Third ACM International Conference on Web Search and Data Mining, New York, USA, pp. 101–110 (2010)
Collins, M., Singer, Y.: Unsupervised models for named entity classification. Empirical Methods in NLP, EMNLP (1999)
Cortes, C., Vapnik, V.: Support-vector Network. Machine Learning 20, 273–297 (1995)
Das, S.R., Chen, M.Y.: Yahoo! for Amazon: Sentiment parsing from small talk on the web. Management Science 53(9), 1375–1388 (2007)
Dave, K., Lawrence, S., Pennock, D.M.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of the 12th International Conference on World Wide Web, New York, USA, pp. 519–528 (2003)
Emadzadeh, E., Nikfarjam, A., Ghauth, K.I., Why, N.K.: Learning Materials Recommendation Using a Hybrid Recommender System with Automated Keyword Extraction. World Applied Sciences Journal 9(11), 1818–4952 (2010) ISSN: 1818–4952
Huang, R., Riloff, E.: Multi-faceted Event Recognition with Bootstrapped Dictionaries. In: NAACL-HLT 2013, Atlanta, Georgia, USA, June 9-14, pp. 41–51 (June 2013)
Joorabchi, A., Mahdi, A.E.: A New Method for Bootstrapping an Automatic Text Classification System Utilizing Public Library Resources. In: 19th Irish Conference on Artificial Intelligence and Cognitive Science, AICS-2008 (2008)
Kennedy, A., Inkpen, D.: Sentiment Classification of Movie Reviews Using Contextual Valence Shifters. In: Computational Intelligence (2006)
Lee, S., Lee, G.G.: G.B.: A bootstrapping Approach for Geographic Named Entity Annotation. Asia Information Retrieval Symposium (2004)
Leksin, V.A., Nikolenko, S.I.: Semi-supervised Tag Extraction in a Web Recommender System. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds.) SISAP 2013. LNCS, vol. 8199, pp. 206–212. Springer, Heidelberg (2013)
Lim, E.P., Sun, A., Marissa, M.: Conceptual Classification of Web Pages using Bootstrapping and Co-Training Strategies. In: Cyberscape Journal, Volume 4 (1). Research Collection School of Information Systems (2006) ISSN: 1675-9281
Lin, F., Cohen, W.W.: The MultiRank Bootstrap Algorithm: Semi-Supervised Political Blog Classification and Ranking Using Semi-Supervised Link Classification. Language Technologies Institute, School of Computer Science, Carnegie Mellon University. Retrieved from (2008), http://www.lti.cs.cmu.edu (access date: October 9, 2013)
Murphy, T and Curran, J. R.: Experiments in Mutual Exclusion Bootstrapping. In: Australasian Language Technology Workshop 2007, pp. 66–74 (2007)
Narayanan, V., Arora, I., Bhatia, A.: Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model. arXiv:1305.6143 (2013)
Naw, N., Hlaing, E.E.: Relevant Words Extraction Method for Recommendation System. International Journal of Emerging Technology and Advanced Engineering 3(1) (January 2013) ISSN: 2250–2459
Niu, C., Li, W., Ding, J., Srihari, R.K.: A Bootstrapping Approach to Named Entity Classification Using Successive Learners. In: 41st Annual Meeting of the ACL (2003)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Park, D.H., Kim, H.K., Kim, J.K.: A Literature Review and Classification of Recommender Systems Research. Social Science 5, 290–294 (2011)
Pronoza, E., Yagunova, E., Lyashin, A.: Restaurant Information Extraction for the Recommendation System. In: Proceedings of the 2nd Workshop on Social and Algorithmic Issues in Business Support: “Knowledge Hidden in Text”, LTC 2013 (2013)
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Ricci, F., Rikach, L., Shapira, B., Kantor, P.: Recommender Systems Handbook, p. 62. Springer, US (2010)
Richardson, S.D.: Bootstrapping Statistical Processing into a Rule-based Natural Language Parser. Microsoft Research, One Microsoft Way, Redmond, WA 98052 (1994), http://research.microsoft.com/apps/pubs/default.aspx?id=69572 (retrieved from: access date: October 9, 2013)
Riloff, E., Jones, R.: Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping. In: Sixteenth National Conference on Artificial Intelligence (AAAI-99), Orlando, Florida, USA (1999)
Saif, H.: Sentiment Analysis of Microblogs. Mining the New World. Technical Report KMI-12-2 (March 2012)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: 1st ACM Conference on Electronic Commerce EC 1999, pp. 158–166 (1999)
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic N-grams as machine learning features for natural language processing. Expert Systems with Applications 41(3), 853–860 (2014), doi:10.1016/j.eswa.2013.08.015
Semeraro, G.: Content-based Recommender Systems: problems, challenges and research directions. In: 8th Workshop on Intelligent Techniques for Web Personalization & Recommender Systems (2010)
Shah, K., Munshi, N. and Reddy, P.: Sentiment Analysis and Opinion Mining of Microblogs (May 5, 2013)
Smith, A.D., Eisner, J.: Bootstrapping Feature-Rich Dependency Parsers with Entropic Priors. In: 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, pp. 667–677 (June 2007)
Thelen, M., Riloff, E.L.: A Bootstrapping Method for Learning Semantic Lexicons Using Extraction Pattern Contexts. In: Empirical Methods in NLP (EMNLP) (2002)
Turney, P.: Thumbs up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, pp. 417–424 (July 2002)
Wang, S., Manning, C.D.: Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012): Short Papers, vol. 2, pp. 90–94 (2012)
Yangarber, R., Grishman, R., Tapanainen, P., Huttunen, S.: Automatic Acquisition of Domain Knowledge for Information Extraction. In: 18th Conference on Computational linguistics (COLING 2000), vol. 2, pp. 940–946 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pronoza, E., Yagunova, E., Volskaya, S., Lyashin, A. (2014). Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Human-Inspired Computing and Its Applications. MICAI 2014. Lecture Notes in Computer Science(), vol 8856. Springer, Cham. https://doi.org/10.1007/978-3-319-13647-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-13647-9_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13646-2
Online ISBN: 978-3-319-13647-9
eBook Packages: Computer ScienceComputer Science (R0)