Neuro-Fuzzy Based Model for Phrase Level Emotion Understanding
Authors: Vadivel Ayyasamy
The present approach deals with the identification of Emotions and classification of Emotional patterns at Phrase-level with respect to Positive and Negative Orientation. The proposed approach considers emotion triggered terms, its co-occurrence terms and also associated sentences for recognizing emotions. The proposed approach uses Part of Speech Tagging and Emotion Actifiers for classification. Here sentence patterns are broken into phrases and Neuro-Fuzzy model is used to classify which results in 16 patterns of emotional phrases. Suitable intensities are assigned for capturing the degree of emotion contents that exist in semantics of patterns. These emotional phrases are assigned weights which supports in deciding the Positive and Negative Orientation of emotions. The approach uses web documents for experimental purpose and the proposed classification approach performs well and achieves good F-Scores.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1339616Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 675
 S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han, Y. Yu, Joint Emotion-topic modeling for social affective text mining, in: The 9th IEEE International Conference on Data Mining, 2009, pp. 699–704.
 F.R. Chaumartin, UPAR7: a knowledge-based system for headline sentiment tagging, in: The 4th International Workshop on Semantic Evaluations, ACL, 2007, pp. 422–425.
 E. Cambria, E. Hussain, C. Havasi, C. Eckl, Affective space: blending common sense and affective knowledge to perform emotive reasoning, Proceedings of the 1st Workshop on Opinion Mining and Sentiment Analysis (WOMSA), 2009.
 Das, D., and Bandyopadhyay, S.: Sentence Level Emotion Tagging on Blog and News Corpora. J. Intelligent System. 19(2), 125-134 (2010).
 Das, D., and Bandyopadhyay, S.: Word to Sentence Level Emotion Tagging for Bengali Blogs. In: ACL-IJCNL.pp. 149-152. Singapore (2009b).
 Ekman, P.: An Argument for Basic Emotions. Cognition and Emotion. 6, 169–200 (1992).
 Ekbal, A., and Bandyopadhyay., S.: Web-based Bengali News Corpus for Lexicon Development and POS Tagging. POLIBITS. 37, 20-29(2008).
 P. Katz, M. Singleton, R. Wicentowski, Swat-MP: the semeval-2007 systems for task 5 and task 14, in: The 4th International Workshop on Semantic Evaluations, ACL, 2007, pp. 308–313.
 A. Kolya, D. Das, A. Ekbal, S. Bandyopadhyay, Identifying event-sentiment association using lexical equivalence and co-reference approaches, in: Workshop on Relational Models of Semantics Collocated with ACL, 2011, pp. 19–27.
 Lin, K., H,-Y., Yang, C., and Chen, H.,-H.: What Emotions do News Articles Trigger in Their Readers? In: SIGIR, pp.733-734(2007).
 Neviarouskaya, A., Prendinger, H., and Ishizuka, M.: Narrowing the Social Gap among People Involved in Global Dialog: Automatic Emotion Detection in Blog Posts, In: Intl. Conf on Weblogs and Social Media, ICWSM.pp. 293-294(2007).
 Peter, D., Turney.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: 40th Annual Meeting of the Association for Computational Linguistics (ACL). pp. 417- 424(2002).
 C. Strapparava, R. Mihalcea, Semeval-2007 task 14: affective text, in: The 4th International Workshop on Semantic Evaluations, ACL, 2007, pp. 70–74.
 Vincent, B., Xu, L., Chesley, P., and Srhari, R., K.: Using Verbs and Adjectives to automatically classify blog sentiment. In: Symposium on Computational approaches to analyzing Weblogs, AAAI-CAAW. pp-27-29 (2006).
 Zhang, Y., Li, Z., Ren, F., and Kuroiwa, S.: A preliminary research of Chinese Emotion classification model. IJCSNS, 8(11), 127-132(2008).