Sentiment Analysis Is a Big Suitcase

IEEE Intelligent Systems

Volumn 32, Issue 6, 2017, Pages 74-80

  Cambria, Erik ^a   Poria, Soujanya ^b   Gelbukh, Alexander ^c   Thelwall, Mike ^d  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c INSTITUTO POLITÉCNICO NACIONAL   (Mexico)

^d UNIVERSITY OF WOLVERHAMPTON   (United Kingdom)

Author keywords

artificial intelligence; computational linguistics; intelligent systems; knowledge representation and reasoning; machine learning; natural language processing; sentiment analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL LINGUISTICS; DATA MINING; INTELLIGENT SYSTEMS; KNOWLEDGE REPRESENTATION; LEARNING ALGORITHMS; LEARNING SYSTEMS;

AFFECTIVE COMPUTING; EMOTION RECOGNITION; KNOWLEDGE REPRESENTATION AND REASONING; NATURAL LANGUAGES; OPINION MINING; RESEARCH PROBLEMS; SENTIMENT ANALYSIS; THREE-LAYER STRUCTURES;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 85038582534     PISSN: 15411672     EISSN: None     Source Type: Journal    
DOI: 10.1109/MIS.2017.4531228     Document Type: Article

References (20)

1. E. Cambria and B. White, "Jumping NLP Curves: A Review of Natural Language Processing Research," IEEE Computational Intelligence Magazine, vol. 9, no. 2, 2014, pp. 48-57.
2. T. Bertaglia and M. das Graças, "Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization," Proc. 26th Int'l Conf. Computational Linguistics (COLING 16), 2016, pp. 112-120.
3. J.C. Reynar and A. Ratnaparkhi, "A Maximum Entropy Approach to Identifying Sentence Boundaries," Proc. 5th Conf. Applied Natural Language Processing, 1997, pp. 16-19.
4. Z. Huang, W. Xu, and K. Yu, "Bidirectional LSTM-CRF Models for Sequence Tagging," arXiv preprint arXiv:1508.01991, 2015.
5. L.A Ramshaw and M.P. Marcus, "Text Chunking Using Transformation-Based Learning," Natural Language Processing Using Very Large Corpora, Springer, 1999, pp. 157-176.
6. M. Kestemont et al., "Lemmatization for Variation-Rich Languages Using Deep Learning," Literary and Linguistic Computing, 2016, p. fqw034.
7. Y.K. Lee and H.T. Ng, "An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation," Proc. ACL-02 Conf. Empirical Methods in Natural Language Processing, vol. 10, 2002, pp. 41-48.
8. E. Cambria et al., "SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives," Proc. 26th Int'l Conf. Computational Linguistics (COLING 16), 2016, pp. 2666-2677.
9. Y. Ma, E. Cambria, and S. Gao, "Label Embedding for Zero-Shot Fine-Grained Named Entity Typing," Proc. 26th Int'l Conf. Computational Linguistics (COLING 16), 2016, pp. 171-180.
10. X. Zhong, A. Sun, and E. Cambria, "Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules," Proc. 55th Ann. Meeting Assoc. for Computational Linguistics (ACL 17), 2017, pp. 420-429.
11. R. Mitkov, R. Evans, and C. Orasan, "A New, Fully Automatic Version of Mitkov's Knowledge-Poor Pronoun Resolution Method," Computational Linguistics and Intelligent Text Processing, Springer, 2002, pp. 168-186.
12. I. Chaturvedi et al., "Bayesian Network Based Extreme Learning Machine for Subjectivity Detection," J. Franklin Inst., 2017; doi:10.1016/j.jfranklin.2017.06.007.
13. F. Mairesse et al., "Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text," J. Artificial Intelligence Research (JAIR), vol. 30, Sept.-Dec. 2007, pp. 457-500.
14. N. Majumder et al., "Deep Learning-Based Document Modeling for Personality Detection from Text," IEEE Intelligent Systems, vol. 32, no. 2, 2017, pp. 74-79.
15. L. Polanyi and A. Zaenen, "Contextual Valence Shifters," Computing Attitude and Affect in Text: Theory and Applications, Springer, 2006, pp. 1-6.
16. S. Poria et al., "A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks," Proc. 26th Int'l Conf. Computational Linguistics (COLING 16), 2016, pp. 1601-1612.
17. M. Schulder and E. Hovy, "Metaphor Detection Through Term Relevance," Proc. 2nd Workshop on Metaphor in NLP, 2014, pp. 18-26.
18. S. Poria et al., "Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis," Proc. IEEE Int'l Conf. Data Mining (ICDM 16), 2016, pp. 439-448.
19. M. Thelwall, "The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength," Cyberemotions: Collective Emotions in Cyberspace, J.A. Holyst, ed., Springer, 2017, pp. 119-134.
20. S. Poria et al., "Sentiment Data Flow Analysis by Means of Dynamic Linguistic Patterns," IEEE Computational Intelligence Magazine, vol. 10, no. 4, 2015, pp. 26-36.