Sentic patterns: Dependency-based rules for concept-level sentiment analysis

Knowledge-Based Systems

Volumn 69, Issue 1, 2014, Pages 45-63

  Poria, Soujanya ^a   Cambria, Erik ^b   Winterstein, Grégoire ^c   Huang, Guang Bin ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

Artificial intelligence; Linguistic rules; Natural language processing; Opinion mining; Sentic computing

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; DATA MINING; DISTILLATION; INFORMATION RETRIEVAL; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; LINGUISTICS; MARKETING; SOCIAL NETWORKING (ONLINE); ABSTRACTING;

ENSEMBLE APPLICATIONS; KNOWLEDGE-BASED TECHNOLOGY; LINGUISTIC RULES; NATURAL LANGUAGE PROCESSING; NATURAL LANGUAGE TEXT; OPINION MINING; SENTIC COMPUTING; SOCIAL MEDIA MARKETINGS; CORPORATE REPUTATIONS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84924582748     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2014.05.005     Document Type: Article

References (60)

1. E. Cambria, B. White, Jumping NLP curves: a review of natural language processing research, IEEE Comput. Intell. Mag. 9 (2) (2014) 48-57.
2. E. Cambria, A. Hussain, Sentic Computing: Techniques, Tools, and Applications, Springer, Dordrecht, Netherlands, 2012.
3. M. Bradley, P. Lang, Affective norms for English words (ANEW): stimuli, instruction manual and affective ratings, Tech. Rep., The Center for Research in Psychophysiology, University of Florida, 1999.
4. C. Strapparava, A. Valitutti, WordNet-Affect: an affective extension of WordNet, in: LREC, Lisbon, 2004, pp. 1083-1086.
5. C. Bazzanella, Emotions, language and context, in: E. Weigand (Ed.), Emotion in Dialogic Interaction, Advances in the Complex, Benjamins, Amsterdam, 2004, pp. 59-76.
6. A. Esuli, F. Sebastiani, SentiWordNet: a publicly available lexical resource for opinion mining, in: LREC, 2006.
7. E. Cambria, D. Olsher, D. Rajagopal, SenticNet 3:a common and common-sense knowledge base for cognition-driven sentiment analysis, in: AAAI, Quebec City, 2014, pp. 1515-1521.
8. A. Tsai, R. Tsai, J. Hsu, Building a concept-level sentiment dictionary based on commonsense knowledge, IEEE Intell. Syst. 28 (2) (2013) 22-30.
9. S. Poria, A. Gelbukh, A. Hussain, N. Howard, D. Das, S. Bandyopadhyay, Enhanced SenticNet with affective labels for concept-based opinion mining, IEEE Intell. Syst. 28 (2) (2013) 31-38.
10. C. Hung, H.-K. Lin, Using objective words in SentiWordNet to improve sentiment classification for word of mouth, IEEE Intell. Syst. 28 (2) (2013) 47-54.
11. C. Bosco, V. Patti, A. Bolioli, Developing corpora for sentiment analysis and opinion mining: a survey and the Senti-TUT case study, IEEE Intell. Syst. 28 (2) (2013) 55-63.
12. A. Weichselbraun, S. Gindl, A. Scharl, Extracting and grounding context-aware sentiment lexicons, IEEE Intell. Syst. 28 (2) (2013) 39-46.
13. L. García-Moya, H. Anaya-Sanchez, R. Berlanga-Llavori, A language model approach for retrieving product features and opinions from customer reviews, IEEE Intell. Syst. 28 (3) (2013) 19-27.
14. R. Xia, C. Zong, X. Hu, E. Cambria, Feature ensemble plus sample selection: a comprehensive approach to domain adaptation for sentiment classification, IEEE Intell. Syst. 28 (3) (2013) 10-18.
15. G. Di Fabbrizio, A. Aker, R. Gaizauskas, Summarizing on-line product and service reviews using aspect rating distributions and language modeling, IEEE Intell. Syst. 28 (3) (2013) 28-37.
16. V. Perez-Rosas, R. Mihalcea, L.-P. Morency, Multimodal sentiment analysis of Spanish online videos, IEEE Intell. Syst. 28 (3) (2013) 38-45.
17. M. Wollmer, F. Weninger, T. Knaup, B. Schuller, Congkai, K. Sagae, L.-P. Morency, YouTube movie reviews: in, cross, and open-domain sentiment analysis in an audiovisual context, IEEE Intell. Syst. 28 (3) (2013) 46-53.
18. E. Cambria, D. Rajagopal, D. Olsher, D. Das, Big social data analysis, in: R. Akerkar (Ed.), Big Data Computing, Chapman and Hall/CRC, 2013, pp. 401-414 (Chapter 13).
19. C. Manning, Part-of-speech tagging from 97% to 100%: is it time for some linguistics?, in: A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, Vol. 6608, Springer, 2011, pp. 171-189.
20. C. Paice, Another stemmer, SIGIR Forum 24 (3) (1990) 56-61.
21. M. Minsky, The Society of Mind, Simon and Schuster, New York, 1986.
22. G. Fauconnier, M. Turner, The Way We Think: Conceptual Blending and the Mind's Hidden Complexities, Basic Books, 2003.
23. C. Lanczos, An iteration method for the solution of the eigenvalue problem of linear differential and integral operators, J. Res. Nat. Bureau Standards 45 (4) (1950) 255-282.
24. C. Eckart, G. Young, The approximation of one matrix by another of lower rank, Psychometrika 1 (3) (1936) 211-218.
25. R. Plutchik, The nature of emotions, Am. Scientist 89 (4) (2001) 344-350.
26. M. Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind, Simon & Schuster, New York, 2006.
27. L. Barrett, Solving the emotion paradox: categorization and the experience of emotion, Personal. Soc. Psychol. Rev. 10 (1) (2006) 20-46.
28. E. Krumhuber, A. Kappas, Moving smiles: the role of dynamic components for the perception of the genuineness of smiles, J. Nonverbal Behav. 29 (1) (2005) 3-24.
29. M. Lewis, I. Granic, Emotion, Development, and Self-Organization: Dynamic Systems Approaches to Emotional Development, Cambridge University Press, 2002.
30. M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience, Harper Perennial, 1991.
31. E. Cambria, P. Gastaldo, F. Bisio, R. Zunino, An ELM-based model for affective analogical reasoning, Neurocomputing Special Issue on Extreme Learning Machines.
32. E. Cambria, G.-B. Huang, Extreme learning machines, IEEE Intell. Syst. 28 (6) (2013) 2-31.
33. S. Ridella, S. Rovetta, R. Zunino, Circular backpropagation networks for classification, IEEE Trans. Neural Networks 8 (1) (1997) 84-97.
34. T.P. Vogl, J. Mangis, A. Rigler, W. Zink, D. Alkon, Accelerating the convergence of the back-propagation method, Biol. Cybernet. 59 (4-5) (1988) 257-263.
35. G.-B. Huang, D.H. Wang, Y. Lan, Extreme learning machines: a survey, Int. J. Mach. Learn. Cybernet. 2 (2) (2011) 107-122.
36. G.-B. Huang, L. Chen, C.-K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17 (4) (2006) 879-892.
37. G.-B. Huang, An insight into extreme learning machines: random neurons, random features and kernels, Cogn. Comput., doi: http://dx.doi.org/10.1007/s12559-014-9255-2.
38. G.-B. Huang, H. Zhou, X. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybernet. Part B: Cybernet. 42 (2) (2012) 513-529.
39. E. Cambria, Y. Xia, A. Hussain, Affective common sense knowledge acquisition for sentiment analysis, in: LREC, Istanbul, 2012, pp. 3580-3585.
40. M. Joshi, C. Rose, Generalizing dependency features for opinion mining, in: ACL/IJCNLP, Singapore, 2009.
41. B. Heerschop, F. Goossen, A. Hogenboom, F. Frasincar, U. Kaymak, F. de Jong, Polarity analysis of texts using discourse structure, in: CIKM, Glasgow, 2011.
42. F. Wang, Y. Wu, L. Qiu, Exploiting discourse relations for sentiment analysis, in: COLING, Mumbai, 2012, pp. 1311-1320.
43. B. Chardon, F. Benamara, Y. Mathieu, V. Popescu, N. Asher, Measuring the effect of discourse structure on sentiment analysis, Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, Vol. 7817, Springer, 2013, pp. 25-37.
44. E. Coppock, D. Beaver, Principles of the exclusive muddle, J. Semantics doi:10.1093/jos/fft007.
45. G. Winterstein, Layered meanings and Bayesian argumentation. the case of exclusives, submitted for publication.
46. L. Karttunen, Presuppositions of compound sentences, Linguistic Inquiry 4 (2) (1973) 169-193.
47. D. Beaver, Presupposition and Assertion in Dynamic Semantics, CSLI Publications, 2008.
48. C. Potts, The Logic of Conventional Implicatures, Oxford University Press, Oxford, 2005.
49. M. Simons, J. Tonhauser, D. Beaver, C. Roberts, What projects and why, in: Semantics and Linguistic Theory (SALT) 20, 2010, pp. 309-327.
50. N. Asher, A. Lascarides, Logics of Conversation, Cambridge University Press, Cambridge, 2003.
51. S. Repp, Negation in Gapping, Oxford University Press, Oxford, 2009.
52. L. Vicente, On the syntax of adversative coordination, Nat. Lang. Linguistic Theory 28 (2) (2010) 381-415.
53. J. Anscombre, O. Ducrot, Deux mais en français, Lingua 43 (1977) 23-40.
54. G. Winterstein, What but-sentences argue for: a modern argumentative analysis of but, Lingua 122 (15) (2012) 1864-1885.
55. J. Jayez, G. Winterstein, Additivity and probability, Lingua 132 (2013) 85-102.
56. B. Pang, L. Lee, Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, in: ACL, Ann Arbor, 2005, pp. 115-124.
57. R. Socher, A. Perelygin, J.Y. Wu, J. Chuang, C.D. Manning, A.Y. Ng, C. Potts, Recursive deep models for semantic compositionality over a sentiment treebank.
58. J. Blitzer, M. Dredze, F. Pereira, Biographies, Bollywood, boom-boxes and blenders: domain adaptation for sentiment classification, in: ACL, Prague, 2007, pp. 440-447.
59. R. Socher, B. Huval, C.D. Manning, A.Y. Ng, Semantic compositionality through recursive matrix-vector spaces, in: EMNLP/CoNLL, Jeju, 2012, pp. 1201-1211.
60. Z. Lin, T. Hwee, M.-Y. Kan, A PDTB-styled end-to-end discourse parser, Natural Language Engineering.