SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives

COLING 2016 - 26th International Conference on Computational Linguistics, Proceedings of COLING 2016: Technical Papers

Volumn , Issue , 2016, Pages 2666-2677

  Cambria, Erik ^a   Poria, Soujanya ^a   Bajpai, Rajiv ^a   Schuller, Björn ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; SEMANTICS; SENTIMENT ANALYSIS;

AUTOMATICALLY GENERATED; BACKGROUND INFORMATION; COMMONSENSE KNOWLEDGE; DIMENSIONALITY REDUCTION; GENERALIZATION CAPABILITY; HIER-ARCHICAL CLUSTERING; HUMAN INTELLIGENCE; SEMANTIC RESOURCES;

DATA MINING;

EID: 85046586891     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (46)

1. Nir Ailon and Bernard Chazelle. 2010. Faster dimension reduction. Communications of the ACM, 53(2): 97-104.
2. Matheus Araújo, Pollyanna Gonçalves, Meeyoung Cha, and Fabríckt Benevenuto. 2014. iFeel: A system that compares and combines sentiment analysis methods. In WWW, pages 75-78.
3. David Balduzzi. 2013. Randomized co-training: from cortical neurons to machine learning and back again. arXiv preprint arXiv: 1310.6536.
4. Pavel Berkhin. 2006. A survey of clustering data mining techniques. Grouping multidimensional data, pages 25-71.
5. Ella Bingham and Heikki Mannila. 2001. Random projection in dimensionality reduction: applications to image and text data. In ACM SIGKDD, pages 245-250.
6. John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, Volume 7, pages 440-447.
7. Felipe Bravo-Marquez, Marcelo Mendoza, and Barbara Poblete. 2014. Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69: 86-99.
8. Erik Cambria and Amir Hussain. 2015. Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham, Switzerland.
9. Erik Cambria and Bebo White. 2014. Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2): 48-57.
10. Erik Cambria, Daniel Olsher, and Dheeraj Rajagopal. 2014. SenticNet 3: A common and common-sense knowledge base for cognition-driven sentiment analysis. In AAAI, pages 1515-1521, Quebec City.
11. Erik Cambria, Jie Fu, Federica Bisio, and Soujanya Poria. 2015. AffectiveSpace 2: Enabling affective intuition for concept-level sentiment analysis. In AAAI, pages 508-514, Austin.
12. Erik Cambria. 2016. Affective computing and sentiment analysis. IEEE Intelligent Systems, 31(2): 102-107.
13. Cicero Nogueira dos Santos and Maira Gatti. 2014. Deep convolutional neural networks for sentiment analysis of short texts. In COLING, pages 69-78.
14. Gilles Fauconnier and Mark Turner. 2003. The Way We Think: Conceptual Blending and the Mind's Hidden Complexities. Basic Books.
15. Gizem Gezici, Rahim Dehkharghani, Berrin Yanikoglu, Dilek Tapucu, and Yucel Saygin. 2013. Su-sentilab: A classification system for sentiment analysis in twitter. In International Workshop on Semantic Evaluation, pages 471-477.
16. Ray Jackendoff. 1976. Toward an explanatory semantic representation. Linguistic Inquiry, 1(1): 89-150.
17. Anil Jain and Richard Dubes. 1988. Algorithms for clustering data. Prentice-Hall, Inc.
18. Honglak Lee, Roger Grosse, Rajesh Ranganath, and A. Y. Ng. 2011. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM, 54(10): 95-103.
19. Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan and Claypool.
20. Yichao Lu, Paramveer Dhillon, Dean P Foster, and Lyle Ungar. 2013. Faster ridge regression via the subsampled randomized hadamard transform. In Advances in Neural Information Processing Systems, pages 369-377.
21. Prem Melville, Wojciech Gryc, and Richard D Lawrence. 2009. Sentiment analysis of blogs by combining lexical knowledge with text classification. In ACM SIGKDD, pages 1275-1284.
22. Marvin Minsky. 1975. A framework for representing knowledge. In Patrick Winston, editor, The psychology of computer vision. McGraw-Hill, New York.
23. Bo Pang and Lillian Lee. 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115-124, Ann Arbor.
24. Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2: 1-135.
25. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In EMNLP, Volume 10, pages 79-86.
26. Livia Polanyi and Annie Zaenen. 2006. Contextual valence shifters. In Computing Attitude and Affect in Text: Theory and Applications, Volume 20 of The Information Retrieval Series, pages 1-10. Springer, Berlin, Germany.
27. Soujanya Poria, Erik Cambria, Alexander Gelbukh, Federica Bisio, and Amir Hussain. 2015. Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Computational Intelligence Magazine, 10(4): 26-36.
28. Soujanya Poria, Erik Cambria, and Alexander Gelbukh. 2016a. Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 108: 42-49.
29. Soujanya Poria, Erik Cambria, Newton Howard, Guang-Bin Huang, and Amir Hussain. 2016b. Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing, 174: 50-59.
30. Soujanya Poria, Iti Chaturvedi, Erik Cambria, and Federica Bisio. 2016c. Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis. In IJCNN.
31. Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi, and Andrea Nuzzolese. 2014. Sentilo: Frame-based sentiment analysis. Cognitive Computation, 7(2): 211-225.
32. David Rumelhart and Andrew Ortony. 1977. The representation of knowledge in memory. In C Anderson, R Spiro, and W Montague, editors, Schooling and the acquisition of knowledge. Erlbaum, Hillsdale, NJ.
33. Tamas Sarlos. 2006. Improved approximation algorithms for large matrices via random projections. In FOCS, pages 143-152.
34. Roger Schänk. 1972. Conceptual dependency: A theory of natural language understanding. Cognitive Psychology, 3: 552-631.
35. Karin Schuler. 2005. VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. thesis, University of Pennsylvania, Computer and Information Science.
36. Barry Smith, editor. 1988. Foundations of Gestalt Theory. Munich and Vienna: Philosophia Verlag.
37. Richard Socher, Brody Huval, Christopher D Manning, and Andrew Y Ng. 2012. Semantic compositionality through recursive matrix-vector spaces. In EMNLP, pages 1201-1211.
38. Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In EMNLP, pages 1642-1654.
39. Robert Speer and Catherine Havasi. 2012. ConceptNet 5: A large semantic network for relational knowledge. In Eduard Hovy, M Johnson, and G Hirst, editors, Theory and Applications of Natural Language Processing, chapter 6. Springer.
40. Carlo Strapparava and Alessandro Valitutti. 2004. WordNet-Affect: An affective extension of WordNet. InLREC, pages 1083-1086, Lisbon.
41. Duyu Tang, Furu Wei, Bing Qin, Ting Liu, and Ming Zhou. 2014a. Coooolll: A deep learning system for twitter sentiment classification. In SemEval, pages 208-212.
42. Duyu Tang, Furu Wei, Nan Yang, Ting Liu, and Bing Qin. 2014b. Learning sentiment-specific word embedding for twitter sentiment classification. In ACL, pages 1555-1565.
43. Joel A Tropp. 2011. Improved analysis of the subsampled randomized hadamard transform. Advances in Adaptive Data Analysis, 3(01n02): 115-126.
44. Amos Tversky. 1977. Features of similarity. Psychological Review, 84(4): 327-352.
45. Anna Wierzbicka. 1996. Semantics: Primes and Universals. Oxford University Press.
46. Hong Yu and Vasileios Hatzivassiloglou. 2003. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In EMNLP, pages 129-136.