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

Social Network Analysis and Mining

Volumn 3, Issue 4, 2013, Pages 1149-1163

  Cataldi, Mario ^a   Ballatore, Andrea ^b   Tiddi, Ilaria ^c   Aufaure, Marie Aude ^a  

^a ECOLE CENTRALE PARIS   (France)

^b UNIVERSITY COLLEGE DUBLIN   (Ireland)

^c OPEN UNIVERSITY   (United Kingdom)

Author keywords

Dependency graphs; Feature detection; Natural language processing; Opinion mining; Sentiment analysis

Indexed keywords

BEHAVIORAL RESEARCH; DATA MINING; DECISION MAKING; NATURAL LANGUAGE PROCESSING SYSTEMS; PROGRAM PROCESSORS;

AUTOMATIC UNDERSTANDING; DEPENDENCY GRAPHS; FEATURE DETECTION; OPINION MINING; PRODUCTS AND SERVICES; SENTIMENT ANALYSIS; STATISTICAL TECHNIQUES; SYNTACTIC DEPENDENCIES;

FEATURE EXTRACTION;

EID: 84907554422     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-013-0119-7     Document Type: Article

References (59)

1. 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
2. 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
3. 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
4. Banerjee M, Capozzoli M, McSweeney L, Sinha D (1999) Beyond kappa: a review of interrater agreement measures. Can J Stat 27(1):3–23
5. 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
6. 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
7. 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
8. Chevalier J, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354
9. Collins MJ (1999). Head-driven statistical models for natural language parsing. PhD thesis, University of Pennsylvania, Philadelphia, PA, USA.
10. 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)
11. 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
12. 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
13. Fellbaum C (ed) (1998) WordNet: an electronic lexical database. MIT Press, Cambridge
14. Fleiss J (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382
15. Ganesan K, Zhai C (2012) Opinion-based entity ranking. Inf Retr 15(2):116–150
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. Kasami T (1965) An efficient recognition and syntax analysis algorithm for context-free languages. Technical Report AFCRL-65-758. Air Force Cambridge Research Laboratory
24. 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
25. 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.
26. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 5(1):1–167
27. 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
28. Marcus MP, Marcinkiewicz MA, Santorini B (1993) Building a large annotated corpus of English: the Penn treebank. Comput Linguist 19(2):313–330
29. 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
30. McDonald R, Nivre J (2011) Analyzing and integrating dependency parsers. Comput Linguist 37(1):197–230
31. Miao Q, Li Q, Dai R (2009) AMAZING: a sentiment mining and retrieval system. Expert Syst Appl 36(3):7192–7198
32. Missen M, Boughanem M, Cabanac G (2012) Opinion mining: reviewed from word to document level. Soc Netw Anal Min pp 1–19
33. Moilanen K, Pulman S (2007) Sentiment composition. In: Proceedings of the recent advances in natural language processing international conference (RANLP 2007), pp 378–382
34. 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
35. 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
36. Nadeau D, Sekine S (2007) A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1):3–26
37. O’Connor P (2010) Managing a hotel’s image on tripadvisor. J Hosp Mark Manag 19(7):754–772
38. 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
39. 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
40. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135
41. 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
42. 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
43. Pekar V, Ou S (2008). Discovery of subjective evaluations of product features in hotel reviews. J Vacat Mark 14(2):145–155
44. 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
45. 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
46. 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
47. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27
48. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval. Inf Process Manag 24:513–523
49. 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
50. 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
51. Turney PD, Littman ML (2003) Measuring praise and criticism: inference of semantic orientation from association. ACM Trans Inf Syst 21(4):315–346
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. Zhou L, Chaovalit P (2008) Ontology-supported polarity mining. J Am Soc Inf Sci Technol 59(1):98–110