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Good location, terrible food: detecting feature sentiment in user-generated reviews

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Abstract

A growing corpus of online informal reviews is generated every day by non-experts, on social networks and blogs, about an unlimited range of products and services. Users do not only express holistic opinions, but often focus on specific features of their interest. The automatic understanding of “what people think” at the feature level can greatly support decision making, both for consumers and producers. In this paper, we present an approach to feature-level sentiment detection that integrates natural language processing with statistical techniques, in order to extract users’ opinions about specific features of products and services from user-generated reviews. First, we extract domain features, and each review is modelled as a lexical dependency graph. Second, for each review, we estimate the polarity relative to the features by leveraging the syntactic dependencies between the terms. The approach is evaluated against a ground truth consisting of set of user-generated reviews, manually annotated by 39 human subjects and available online, showing its human-like ability to capture feature-level opinions.

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Notes

  1. http://www.tripadvisor.com.

  2. http://github.com/ucd-spatial/Datasets.

  3. This step is performed with the jExSLI tool, available at http://hlt.fbk.eu/en/technology/jExSLI.

  4. Where JJ means adjective, CC coordinating conjunction, NN noun, IN preposition, PDT predeterminer, and DT determiner. A complete list of the categories has been defined by Marcus et al. (1993).

  5. In some sentences, the semantic and syntactic representation may not correspond. For a detailed discussion, see De Marneffe et al. (2006).

  6. In total, the system detected 33 features for the considered domain. The 19 unique features randomly selected for the experimental evaluation are: room, staff, location, breakfast, place, service, bathroom, restaurant, area, desk, view, shower, bed, pool, city, Internet, reception, rate, parking.

  7. http://github.com/ucd-spatial/Datasets.

  8. http://github.com/ucd-spatial/Datasets.

References

  • Annett M, Kondrak G (2008) A comparison of sentiment analysis techniques: polarizing movie blogs. In: Advances in artificial intelligence, vol 5032. Springer, LNCS, pp 25–35

  • Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and ppinion mining. In: Proceedings of the seventh conference on international language resources and evaluation (LREC’10), pp 2200–2204

  • Baldwin T, Lui M (2010) Language identification: the long and the short of the matter. In: Human language technologies: The 2010 annual conference of the North American chapter of the association for computational linguistics, ACL, pp 229–237

  • Banerjee M, Capozzoli M, McSweeney L, Sinha D (1999) Beyond kappa: a review of interrater agreement measures. Can J Stat 27(1):3–23

    Article  MathSciNet  MATH  Google Scholar 

  • Beineke P, Hastie T, Manning C, Vaithyanathan S (2004) Exploring sentiment summarization. In: Proceedings of the AAAI spring symposium on exploring attitude and affect in text: theories and applications, AAAI, pp 1–4

  • Carvalho P, Sarmento L, Silva M, de Oliveira E (2009) Clues for detecting irony in user-generated contents: oh...!! It’s so easy ;-). In: Proceedings of the 1st international CIKM workshop on topic-sentiment analysis for mass opinion, ACM, pp 53–56

  • Chen L, Qi L (2011) Social opinion mining for supporting buyers’ complex decision making: exploratory user study and algorithm comparison. Soc Netw Anal Min 1(4):301–320

    Article  MathSciNet  Google Scholar 

  • Chevalier J, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354

    Article  Google Scholar 

  • Collins MJ (1999). Head-driven statistical models for natural language parsing. PhD thesis, University of Pennsylvania, Philadelphia, PA, USA.

  • Dawes J (2008) Do data characteristics change according to the number of scale points used? An experiment using 5 point, 7 point and 10 point scales. Int J Mark Res 51(1)

  • De Marneffe MC, Maccartney B, Manning CD (2006) Generating typed dependency parses from phrase structure parses. In: Proceedings of the seventh conference on international language resources and evaluation (LREC 2006), pp 449–454

  • Ding X, Liu B (2010) Resolving object and attribute coreference in opinion mining. In: Proceedings of the 23rd international conference on computational linguistics, ACL, pp 268–276

  • Fellbaum C (ed) (1998) WordNet: an electronic lexical database. MIT Press, Cambridge

  • Fleiss J (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382

    Article  Google Scholar 

  • Ganesan K, Zhai C (2012) Opinion-based entity ranking. Inf Retr 15(2):116–150

    Article  Google Scholar 

  • Godbole N, Srinivasaiah M, Skiena S (2007) Large-scale sentiment analysis for news and blogs. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM), pp 219–222

  • Hatzivassiloglou V, McKeown KR (1997) Predicting the semantic orientation of adjectives. In: Proceedings of the eighth conference on European chapter of the association for computational linguistics, ACL, EACL ’97, pp 174–181

  • Hiroshi K, Tetsuya N, Hideo W (2004) Deeper sentiment analysis using machine translation technology. In: Proceedings of the 20th international conference on computational linguistics, ACL, COLING ’04, pp 1–7

  • Holz F, Teresniak S (2010) Towards automatic detection and tracking of topic change. In: Computational linguistics and intelligent text processing, vol 6008. Springer, LNCS, pp 327–339

  • Hu M, Liu B (2004a) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’04, ACM, pp 168–177

  • Hu M., Liu B. (2004b) Mining opinion features in customer reviews. In: Proceedings of the 19th national conference on artifical intelligence (AAAI’04), AAAI, pp 755–760)

  • Kannan K, Goyal M, Jacob G (2012) Modeling the impact of review dynamics on utility value of a product. Soc Netw Anal Min, pp 1–18

  • Kasami T (1965) An efficient recognition and syntax analysis algorithm for context-free languages. Technical Report AFCRL-65-758. Air Force Cambridge Research Laboratory

  • Klein D, Manning CD (2003) Accurate unlexicalized parsing. In: Proceedings of the 41st annual meeting on association for computational linguistics, ACL, ACL ’03, pp 423–430

  • Lipsman A (2007) Online consumer-generated reviews have significant impact on offline purchase behavior (comScore, Inc. and The Kelsey Group). URL:http://www.comscore.com/Insights/Press_Releases/2007/11/Online_Consumer_Reviews_Impact_Offline_Purchasing_Behavior.

  • Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 5(1):1–167

    Article  Google Scholar 

  • Liu B, Hu M, Cheng J (2005). Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of the 14th international conference on World Wide Web, WWW ’05, ACM, pp 342–351

  • Marcus MP, Marcinkiewicz MA, Santorini B (1993) Building a large annotated corpus of English: the Penn treebank. Comput Linguist 19(2):313–330

    Google Scholar 

  • Matsumoto S, Takamura H, Okumura M (2005) Sentiment classification using word sub-sequences and dependency sub-trees. In: Proceedings of the 9th Pacific-Asia conference on advances in knowledge discovery and data mining, PAKDD’05. Springer, Berlin, pp 301–311

  • McDonald R, Nivre J (2011) Analyzing and integrating dependency parsers. Comput Linguist 37(1):197–230

    Article  Google Scholar 

  • Miao Q, Li Q, Dai R (2009) AMAZING: a sentiment mining and retrieval system. Expert Syst Appl 36(3):7192–7198

    Article  Google Scholar 

  • Missen M, Boughanem M, Cabanac G (2012) Opinion mining: reviewed from word to document level. Soc Netw Anal Min pp 1–19

  • Moilanen K, Pulman S (2007) Sentiment composition. In: Proceedings of the recent advances in natural language processing international conference (RANLP 2007), pp 378–382

  • Morinaga S, Yamanishi K, Tateishi K, Fukushima T (2002). Mining product reputations on the Web. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’02,m ACM, pp 341–349

  • Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web (WWW 2012), ACM, pp 191–200

  • Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1):3–26

    Article  Google Scholar 

  • O’Connor P (2010) Managing a hotel’s image on tripadvisor. J Hosp Mark Manag 19(7):754–772

    Google Scholar 

  • Oelke D, Hao MC, Rohrdantz C, Keim DA, Dayal U, Haug LE, Janetzko H (2009) Visual opinion analysis of customer feedback data. In: IEEE VAST, pp 187–194

  • Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics, ACL, ACL ’04, pp 271–278

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

    Article  Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, ACL, EMNLP ’02, vol 10, pp 79–86

  • Pedersen T, Kolhatkar V (2009) WordNet::SenseRelate::AllWords: a broad coverage word sense tagger that maximizes semantic relatedness. In: The 2009 annual conference of the North American chapter of the association for computational linguistics, ACL, pp 17–20

  • Pekar V, Ou S (2008). Discovery of subjective evaluations of product features in hotel reviews. J Vacat Mark 14(2):145–155

    Article  Google Scholar 

  • Ponzetto SP, Strube M (2007). An API for measuring the relatedness of words in Wikipedia. In: Proceedings of the 45th annual meeting of the association for computational linguistics on interactive poster and demonstration sessions, ACL, pp 49–52

  • Popescu AM, Etzioni O (2005) Extracting product features and opinions from reviews. In: Proceedings of the conference on human language technology and empirical methods in natural language processing, ACL, HLT ’05, pp 339–346

  • Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment Lexicon through double propagation. In: Proceedings of the 21st international joint conference on artifical intelligence. Morgan Kaufmann Publishers Inc., Burlington, pp 1199–1204

  • Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27

    Article  Google Scholar 

  • Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24:513–523

    Google Scholar 

  • Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. In: Proceedings of the 17th international conference on World Wide Web, ACM, pp 111–120

  • Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, ACL, pp 417–424

  • Turney PD, Littman ML (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346

    Article  Google Scholar 

  • Warschauer M, Black R, Chou Y (2010). Online Englishes. In: Kirkpatrick T (ed) The Routledge Handbook of World Englishes. Routledge, New York, pp 490–505

  • Wu Y, Wei F, Liu S, Au N, Cui W, Zhou H, Qu H (2010). OpinionSeer: interactive visualization of hotel customer feedback. IEEE Trans Vis Comput Gr 16(6):1109–1118

    Article  Google Scholar 

  • Ye Q, Law R, Gu B (2009) The impact of online user reviews on hotel room sales. Int J Hosp Manag 28(1):180–182

    Article  Google Scholar 

  • Zhai Z, Liu B, Xu H, Jia P (2011a) Clustering product features for opinion mining. In: Proceedings of the 4th ACM international conference on web search and data mining, ACM, pp 347–354

  • Zhai Z, Liu B, Xu H, Jia P (2011b) Constrained LDA for grouping product features in opinion mining. Adv Know Discov Data Min, pp 448–459

  • Zhang L, Liu B (2011) Identifying noun product features that imply opinions. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: short papers, vol 2, pp 575–580

  • Zhang L, Liu B, Lim S, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: Proceedings of the 23rd international conference on computational linguistics: posters, ACL, pp 1462–1470

  • Zhou L, Chaovalit P (2008) Ontology-supported polarity mining. J Am Soc Inf Sci Technol 59(1):98–110

    Article  Google Scholar 

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Authors and Affiliations

  1. École Centrale Paris, Paris, France

    Mario Cataldi & Marie-Aude Aufaure

  2. University College Dublin, Dublin, Ireland

    Andrea Ballatore

  3. Knowledge Media Institute, The Open University , Milton Keynes, UK

    Ilaria Tiddi

Authors
  1. Mario Cataldi
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  2. Andrea Ballatore
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  3. Ilaria Tiddi
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  4. Marie-Aude Aufaure
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Correspondence to Mario Cataldi.

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Cataldi, M., Ballatore, A., Tiddi, I. et al. Good location, terrible food: detecting feature sentiment in user-generated reviews. Soc. Netw. Anal. Min. 3, 1149–1163 (2013). https://doi.org/10.1007/s13278-013-0119-7

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