nach oben

Erschienen in:

2014 | OriginalPaper | Buchkapitel

Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System

verfasst von : Ekaterina Pronoza, Elena Yagunova, Svetlana Volskaya

Erschienen in: Statistical Language and Speech Processing

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper corpus-based information extraction and opinion mining method is proposed. Our domain is restaurant reviews, and our information extraction and opinion mining module is a part of a Russian knowledge-based recommendation system.

Our method is based on thorough corpus analysis and automatic selection of machine learning models and feature sets. We also pay special attention to the verification of statistical significance.

According to the results of the research, Naive Bayes models perform well at classifying sentiment with respect to a restaurant aspect, while Logistic Regression is good at deciding on the relevance of a user’s review.

The approach proposed can be used in similar domains, for example, hotel reviews, with data represented by colloquial non-structured texts (in contrast with the domain of technical products, books, etc.) and for other languages with rich morphology and free word order.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Informal Mathematical Discourse Parsing with Conditional Random Fields

http://scikit-learn.org

It should be noted that the classifier (“model + feature set” combination) with the highest rank does not necessarily demonstrate the highest average weighted F1 score. The classes 0, 1, 2 and 3 assigned to the classifiers in this paper are based on their ranks (according to non-parametric Holm-Bonferroni test) and not F1 scores.

Baseline features set is considered the simplest one, while Extended_All – the most complex one. MNB and NB models are considered the simplest models, Perceptron – a more complex one, and LogReg and linear SVM – the most complex ones (in fact, they are both similar to Perceptron but their training is more computationally expensive [5]). MNB and NB classifiers are considered similar in the degree of “simplicity” as well as LogReg and linear SVM. A simple model with complex features is considered simpler than a complex model with simple (e.g., baseline) features.

Aston, N., Liddle, J., Hu, W.: Twitter sentiment in data streams with perceptron. J. Comput. Commun. 2, 11–16 (2014)CrossRef

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, 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: Proceedings of the International Conference on Information and Knowledge Management (CIKM) (2010)

Collobert, R., Bengio, S.: Links between Perceptrons, MLPs and SVMs. In: Proceedings of the 21th International Conference on Machine Learning (2004)

Das, S.R., Chen, M.Y.: Yahoo! for Amazon: sentiment parsing from small talk on the web. Manage. Sci. 53(9), 1375–1388 (2007)CrossRef

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, pp. 519–528 (2003)

Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 241–249. Association for Computational Linguistics (2010)

Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MATHMathSciNet

10.

Devitt, A., Ahmad, K.: Is there a language of sentiment? An analysis of lexical resources for sentiment analysis. Lang. Resour. Eval. 47(2), 475–511 (2013)CrossRef

11.

Emadzadeh, E., Nikfarjam, A., Ghauth, K.I., Why, N.K.: Learning materials recommendation using a hybrid recommender system with automated keyword extraction. World Appl. Sci. J. 9(11), 1260–1271 (2010)

12.

Gatterbauer, W., Bohunsky, P., Herzog, M., Krüpl, B., Pollak, B.: Towards domain-independent information extraction from web tables. In: Proceedings of the 16th International Conference on World Wide Web, pp. 71–80 (2007)

13.

Iman, R.L., Davenport, J.M.: Approximations of the critical region of the Friedman statistic. Commun. Stat. 18, 571–595 (1980)CrossRef

14.

Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Comput. Intell. 22(2), 110–125 (2006)CrossRefMathSciNet

15.

Kotelnikov, M., Klekovkina, M.: The automatic sentiment text classification method based on emotional vocabulary. In: RCDL’2012 (2012)

16.

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)CrossRef

17.

Li, Y., Nie, J., Zhang, Y., Wang, B., Yan, B., Weng, F.: Contextual recommendation based on text mining. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010): Poster Volume, pp. 692–700 (2010)

18.

Liu, J., Seneff, S.: Review sentiment scoring via a parse-and-paraphrase paradigm. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, pp. 161–169 (2009)

19.

Marchand, M., Ginsca, A.L., Besançon, R., Mesnard, O.: [LVIC-LIMSI]: using syntactic features and multi-polarity words for sentiment analysis in twitter. In: Proceedings of the 7th International Workshop on Semantic Evaluation, pp. 418–424 (2013)

20.

Narayanan, V., Arora, I., Bhatia, A.: Fast and accurate sentiment classification using an enhanced Naive Bayes model. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., Yao, X. (eds.) IDEAL 2013. LNCS, vol. 8206, pp. 194–201. Springer, Heidelberg (2013)CrossRef

21.

Naw, N., Hlaing, E.E.: Relevant words extraction method for recommendation system. Int. J. Emer. Technol. Adv. Eng. 3(1), 680–685 (2013)

22.

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)

23.

Pak, A., Paroubek, P.: Language independent approach to sentiment analysis. Komp’uternaya Lingvistika i Intellektualnie Tehnologii: po materialam ezhegodnoy mezhdunarodnoy konferencii “Dialog”, vol. 11(18), RGHU, Moscow, pp. 37–50 (2012)

24.

Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRef

25.

Park, D.H., Kim, H.K., Kim, J.K.: A literature review and classification of recommender systems research. Soc. Sci. 5, 290–294 (2011)

26.

Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, LNCS, vol. 4321, pp. 325–341. Springer, Heildelberg (2007)CrossRef

27.

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)

28.

Ricci, F., Rokach, L., Shapira, B., Kantor, P.: Recommender Systems Handbook. Springer, New York (2011)CrossRefMATH

29.

Saif, H.: Sentiment analysis of microblogs. Mining the New World. Technical Report KMI-12-2 (2012)

30.

Sarawagi, S.: Information extraction. Found. Trends Databases 1(3), 261–377 (2007)CrossRef

31.

Shah, K., Munshi, N., Reddy, P.: Sentiment Analysis and Opinion Mining of Microblogs. In: University of Illinois at Chicago, Course CS 583 - Data Mining and Text Mining (2013). http://www.cs.uic.edu/~preddy/dm1.pdf

32.

Sharma, A., Dey, S.: An artificial neural network based approach for sentiment analysis of opinionated text. In: Proceedings of the 2012 ACM Research in Applied Computation Symposium, pp. 37–42 (2012)

33.

Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic n-grams as machine learning features for natural language processing. Expert Syst. Appl. 41(3), 853–860 (2014)CrossRef

34.

Socher, R., Perelygin, A., Wy, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2013)

35.

Turmo, J., Ageno, A., Català, N.: Adaptive information extraction. ACM Comput. Surv. 38(2), 3 (2006)CrossRef

36.

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), pp. 417–424 (2002)

37.

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), vol. 2, pp. 90–94 (2012)

Titel: Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System
verfasst von: Ekaterina Pronoza
Elena Yagunova
Svetlana Volskaya
Verlag: Springer International Publishing
Buch: Statistical Language and Speech Processing
Print ISBN: 978-3-319-11396-8

Electronic ISBN: 978-3-319-11397-5

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-11397-5_21

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"