ICTACT Journal on Soft Computing

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Real-Time Emotion Recognition of Twitter Posts using a Hybrid Approach


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1 Department of Computer Engineering, Pune Institute of Computer Technology, India
     

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The analysis of social media posts is a challenging task, particularly the recognition of user emotions. Text is one of the most common mediums used by humans to express emotion, particularly on social media platforms. As emotions play a pivotal role in human interaction, the ability to recognize them by analyzing textual content has various applications in human-computer interaction (HCI) and natural language processing (NLP). Previous studies on emotion classification used bag-of-words classifiers or deep learning on static Twitter data. Our proposed model is a hybrid approach that uses a combination of keyword-based and learning-based models to perform textual emotion recognition on Twitter posts obtained in real-time. Textual feature extraction is carried out by standard Natural Language Processing (NLP) techniques such as Part-of-Speech (PoS) tagging and topic modeling along with classification done using the random forest algorithm. Results show that our proposed model performs better in comparison to the traditional Unison model with an average accuracy that approximates to 88.39%.

Keywords

Emotion Recognition, Text Mining, Random Forest, Natural Language Processing, POS Tagging, Topic Modeling.
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  • C. Darwin, “The Expression of the Emotions in Man and Animals”, John Murray, 1872.

  • P. Ekman, “An Argument for Basic Emotions”, Cognition and Emotion, Vol. 6, No. 3-4, pp. 169-200, 1992.

  • R. Plutchik, “Emotion: Theory, Research, and Experience”, Academic Press, 1980.

  • Random Forest Classification, Available at: https://towardsdatascience.com/random-forest-classification-and-its-implementation-d5d840dbead0

  • M.D. Munezero, C.S. Montero, E. Sutinen and J. Pajunen, “Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text”, IEEE Transactions on Affective Computing, Vol. 5, No. 2, pp. 101-111, 2014.

  • N. Colneric and J. Demsar, “Emotion Recognition on Twitter: Comparative Study and Training a Unison Model”, IEEE Transactions on Affective Computing (Early Access), pp. 1, 2018.

  • S.M. Mohammad and S. Kiritchenko, “Using Hashtags to Capture Fine Emotion Categories from Tweets”, Computational Intelligence, Vol. 31, No. 2, pp. 301-326, 2015.

  • B. Plank and D. Hovy, “Personality Traits on Twitter-or-How to Get 1,500 Personality Tests in a Week”, Proceedings of 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 92-98, 2015.

  • X. Liu, J. Gao, X. He, L. Deng, K. Duh and Y.Y. Wang, “Representation Learning using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval”, Proceedings of International Conference on North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 912-921, 2015.

  • O. Irsoy and C. Cardie, “Opinion Mining with Deep Recurrent Neural Networks”, Proceedings of International Conference on Empirical Methods in Natural Language Processing, pp. 720-728, 2014.

  • S.M. Mohammad, X. Zhu, S. Kiritchenko and J. Martin, “Sentiment, Emotion, Purpose, and Style in Electoral Tweets”, Information Processing and Management, Vol. 51, No. 4, pp.480-499, 2015.

  • J. Schnebly and S. Sengupta, “Random Forest Twitter Bot Classifier”, Proceedings of 9th Annual Workshop and Conference on Computing and Communication, pp. 506-512, 2019.

  • Y. Hegde and S.K. Padma, “Sentiment Analysis using Random Forest Ensemble for Mobile Product Reviews in Kannada”, Proceedings of IEEE International Conference on Advance Computing, pp. 777-782, 2017.

  • N. Alswaidan and Mohamed El Bachir Menai, “A Survey of State-of-The-Art Approaches for Emotion Recognition in Text”, Knowledge and Information Systems, Vol. 62, pp. 2937-2987, 2020.

  • S. Park, B. Bae and Y. Cheong, “Emotion Recognition from Text Stories Using an Emotion Embedding Model”, Proceedings of IEEE International Conference on Big Data and Smart Computing, pp. 579-583, 2020.

  • Ahmad Fakhri Ab. Nasir, Eng Seok Nee, Chun Sern Choong, Ahmad Shahrizan, Abdul Ghani, “Text-Based Emotion Prediction System using Machine Learning Approach”, Proceedings of IOP Conference Series: Materials Science and Engineering, pp. 1-13, 2020.

  • Vong Anh Ho, Duong Huynh Cong Nguyen, Danh Hoang Nguyen, Linh Thi Van Pham, Duc Vu Nguyen, Kiet Van Nguyen and Ngan Luu Thuy Nguyen, “Emotion Recognition for Vietnamese Social Media Text”, Proceedings of International Conference of the Pacific Association for Computational Linguistics, pp. 319-333, 2019.

  • H.A. Ruposh and M.M. Hoque, “A Computational Approach of Recognizing Emotion from Bengali Texts”, Proceedings of 5th International Conference on Advances in Electrical Engineering, pp. 1-7, 2019.

  • M. Suhasini and B. Srinivasu, “Emotion Detection Framework for Twitter Data using Supervised Classifiers”, International Journal of Computer Applications, Vol. 5, No. 3, pp. 1-12, 2020.

  • J. Golbeck, C. Robles, M. Edmondson and K. Turner, “Predicting Personality from Twitter”, Proceedings of IEEE 3rd International Conference on Privacy, Security, Risk and Trust, pp. 149-156, 2011.

  • Sepandar D. Kamvar and Jonathan Harris, “We Feel Fine and Searching the Emotional Web”, Proceedings of 4th ACM International Conference on Web Search and Data Mining, pp. 117-126, 2011.

  • Johan Bollen, Huina Mao and Xiaojun Zeng, “Twitter Mood Predicts the Stock Market”, Journal of Computational Science, Vol. 2, No. 1, pp. 1-8, 2011.

  • Flor Miriam Plaza-Del-Arco, M. Teresa Martín-Valdivia, L. Alfonso Urena-Lopez and Ruslan Mitkov, “Improved Emotion Recognition in Spanish Social Media through Incorporation of Lexical Knowledge”, Future Generation Computer Systems, Vol. 110, pp. 1000-1008, 2019.

  • B. Nejat, G. Carenini, and R. Ng, “Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis”, Proceedings of 18th Annual Meeting on Discourse and Dialogue, pp. 289-298, 2017.

  • S.M. Mohammad and P.D. Turney, “Emotions Evoked by Common Words and Phrases: using Mechanical Turk to Create an Emotion Lexicon”, Proceedings of Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34, 2010.


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  • Real-Time Emotion Recognition of Twitter Posts using a Hybrid Approach

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Authors

Anjali Deshpande
Department of Computer Engineering, Pune Institute of Computer Technology, India
Ratnamala Paswan
Department of Computer Engineering, Pune Institute of Computer Technology, India

Abstract


The analysis of social media posts is a challenging task, particularly the recognition of user emotions. Text is one of the most common mediums used by humans to express emotion, particularly on social media platforms. As emotions play a pivotal role in human interaction, the ability to recognize them by analyzing textual content has various applications in human-computer interaction (HCI) and natural language processing (NLP). Previous studies on emotion classification used bag-of-words classifiers or deep learning on static Twitter data. Our proposed model is a hybrid approach that uses a combination of keyword-based and learning-based models to perform textual emotion recognition on Twitter posts obtained in real-time. Textual feature extraction is carried out by standard Natural Language Processing (NLP) techniques such as Part-of-Speech (PoS) tagging and topic modeling along with classification done using the random forest algorithm. Results show that our proposed model performs better in comparison to the traditional Unison model with an average accuracy that approximates to 88.39%.

Keywords


Emotion Recognition, Text Mining, Random Forest, Natural Language Processing, POS Tagging, Topic Modeling.

References