CONVOLUTIONAL NEURAL NETWORK MULTI-EMOTION CLASSIFIERS

(Received: 2019-04-19, Revised: 2019-06-10 , Accepted: 2019-06-26)
Natural languages are universal and flexible, but cannot exist without ambiguity. Having more than one attitude and meaning in the same phrase context is the main cause for word or phrase ambiguity. Most previous work on emotion analysis has only covered single-label classification and neglected the presence of multiple emotion labels in one instance. This paper presents multi-emotion classification in Twitter based on Convolutional Neural Networks (CNNs). The applied features are emotion lexicons, word embeddings and frequency distribution. The proposed networks performance is evaluated using state-of-the-art classification algorithms, achieving a hamming score range from 0.46 to 0.52 on the challenging SemEval2018 Task E-c.
  1. X. Quan, Q. Wang, Y. Zhang, L. Si and L. Wenyi, "Latent Discriminative Models for Social Emotion Detection with Emotional Dependency," ACM Transactions on Information Systems (TOIS), vol. 34 no. 1, p. 2, 2014.
  2. J. M. Nareshpalsingh and H. N. Modi, "Multi-label Classification Methods: A Comparative Study," International Research Journal of Engineering and Technology (IRJET), vol. 04, no. 12, December 2017.
  3. C. N. N. Kamath, S. S. Bukhari and A. Dengel, "Comparative Study between Traditional Machine Learning and Deep Learning Approaches for Text Classification," Proc. of the ACM Symposium Conference, pp. 1-11, 2018.
  4. M. Haggag, S. Fathy and N. Elhaggar, "Ontology-based Textual Emotion Detection," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 9, pp. 239- 246, 2015.
  5. Y. Cao, P. Zhang and A. Xiong, "Sentiment Analysis Based on Expanded Aspect-and Polarity-Ambiguous Word Lexicon," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 2, 2015.
  6. L. Flekova, E. Ruppert and D. P. Pietro, "Analyzing Domain Suitability of a Sentiment Lexicon by Identifying Distributionally Bipolar Words," Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2015), pp. 77–84, Lisboa, Portugal, September 2015.
  7. D. M. El-Din, H. M. O. Mokhtar and O. Ismael, "Online Paper Review Analysis," International Journal of Advanced Computer Science and Applications (IJACSA), vol. 6, no. 9, 2015.
  8. S. Mohammad, E. Shutova and P. Turney, "Metaphor As a Medium for Emotion: An Empirical Study," Proceedings of the Joint Conference on Lexical and Computational Semantics, pp. 23-33, Berlin, Germany, 2016.
  9. E. Cambria, J. Fu, F. Bisio and S. Poria, "AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis," Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 508-514, Austin, 2015.
  10. Y. Wang and A. Pal, "Detecting Emotions in Social Media: A Constrained Optimization Approach," Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pp. 996-1002, Buenos Aires, Argentina, 2015.
  11. P. Sobhani, S. M. Mohammad and S. Kiritchenko, "Detecting Stance in Tweets and Analyzing Its Interaction with Sentiment," Proceedings of the Joint Conference on Lexical and Computational Semantics, pp. 159–169, Berlin, Germany, August 2016.
  12. N. Majumder, S. Poria, A. Gelbukh and E. Cambria, "Deep Learning-based Document Modeling for Personality Detection From Text," IEEE Intelligent Systems, vol. 32, no. 2, pp. 74-79, 2017.
  13. R. Oramas, M. L. Barron-Estrada, R. Zatarain-Cabada and S. L. Ramírez-Ávila, "A Corpus for Sentiment Analysis and Emotion Recognition for a Learning Environment," Proc. of the 18th IEEE International Conference on Advanced Learning Technologies (ICALT), pp. 431-435, Mumbai, 2018.
  14. M. Suhasini and S. Badugu, "Two Step Approach for Emotion Detection on Twitter Data," International Journal of Computer Applications, vol. 179, no. 53, pp. 12 –19, June 2018.
  15. S. Mohammad, S. Kiritchenko, X. Zhu and J. Martin. "Sentiment, Emotion, Purpose and Style in Electoral Tweets," Information Processing and Management, vol. 51, no. 4, pp. 480–499, July 2015.
  16. X. Sun, C. Sun, C. Quan, F. Ren, F. Tian and K. Wang, "Fine-grained Emotion Analysis Based on Mixed Model for Product Review," International Journal of Networked and Distributed Computing, vol. 5, no. 1, pp. 1–11, January 2017.
  17. B. Gaind, V. Syal and S. Padgalwar, "Emotion Detection and Analysis on Social Media," Proceedings of the International Conference on Recent Trends in Computational Engineering and Technologies (ICTRCET’18), Bengaluru, India, May 2018.
  18. S. Mohammad, F. B. Marquez, M. Salameh and S. Kiritchenko, "Semeval-2018: Affect in Tweets," Proceedings of the International Workshop on Semantic Evaluation (SemEval-2018), New Orleans, LA, USA, June 2018.
  19. S. Mohammad and P. Turney, "Crowdsourcing a Word-Emotion Association Lexicon," Computational Intelligence, vol. 29, no. 3, pp. 436-465, 2013.
  20. S. Mohammad and P. Turney, "Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon," Proceedings of the NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, LA, California, June 2010.
  21. S. Mohammad, "#Emotional Tweets," The 1st Joint Conference on Lexical and Computational Semantics, vol. 1 (Proceedings of the Main Conference and the Shared Task) and vol. 2 (Proceedings of the 6th International Workshop on Semantic Evaluation (SemEval 2012)), Montr'eal, Canada, pp. 246-255, 7-8 June 2012.
  22. S. Mohammad, "Obtaining Reliable Human Ratings of Valence, Arousal and Dominance for 20,000 English Words," Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.
  23. V. A. Kharde and S. S. Sonawane, "Sentiment Analysis of Twitter Data: A Survey of Techniques," International Journal of Computer Applications, vol. 139, no. 11, pp. 5-15, April 2016.
  24. T. Mikolov, G. Corrado, K. Chen and J. Dean, "Efficient Estimation of Word Representations in Vector Space," Proceedings of the International Conference on Learning Representations (ICLR 2013), pp. 1–12, 2013.
  25. E. M. Alshari, A. Azman, S. Doraisamy, N. Mustapha and M. Alkeshr, "Improvement of Sentiment Analysis based on Clustering of Word2Vec Features," Proc. of the 28th International Workshop on Database and Expert System Applications, 2017.
  26. K. K. Lurz, Natural Language Processing in Artificial Neural Network Sentence Analysis in Medical Papers, Master Thesis, Department of Astronomy and Theoretical Physics, Lund University, June 11, 2018.
  27. F. Chollet, "keras,"[Online], Available: GitHub. https://github.com/fchollet/keras, 2015.
  28. Python Software Foundation,[Online], Available: http://www.python.org, 2019.
  29. J. Perkins, Python 3 Text Processing with NLTK 3 Cookbook, Packt Publishing, 2014.
  30. N. Hardeniya, "NLTK Essentials Build Cool NLP and Machine Learning Applications Using NLTK and Other Python Libraries," July 2015.
  31. A. F. Agarap, "Deep Learning Using Rectified Linear Units (ReLUs)," arXiv:1803.08375, 2018.
  32. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Proceedings of the 3rd International Conference on Learning Representations, 2014.
  33. S. Mannor, D. Peleg and R. Rubinstein, "The Cross-entropy Method for Classification," Proceedings of the 22nd International Conference on Machine Learning (ICML '05), pp. 561-568, Bonn, Germany, August 07 - 11, 2005.
  34. S. Baker and A. Korhonen, "Initializing Neural Networks for Hierarchical Multi-label Text Classification," Association for Computational Linguistics (BioNLP 2017), pp. 307-315, August 2017.
  35. D. Ganda and R. Buch, "A Survey on Multi-label Classification," Recent Trends in Programming Languages, vol. 5, no. 1, pp. 19-23, August 2018.
  36. S. R. Khade and S. R. Balwan. "Study and Analysis of Multi-label Classification Methods in Data Mining," International Journal of Computer Applications, vol. 159, no. 9, February 2017.
  37. J. A. Swets, "ROC Analysis Applied to the Evaluation of Medical Imaging Techniques," Invest. Radiol., vol. 14, no. 2, pp. 109-121, 1979.
  38. J. A. Hanley, "Receiver Operating Characteristic (ROC) Methodology: The State-of-the-Art," Crit. Rev. Diagn. Imaging, vol. 29, no. 3, pp. 307-335, 1989.