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Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networks

Authors: B. Golchin, N. Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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References:


[1] Buettner, Ricardo. Proceedings of the annual hawaii international conference on system sciences. kauai, hawaii. In Proceedings of the Annual Hawaii International Conference on System Sciences. Kauai, Hawaii, Vol. 10, 2015.
[2] Liu, Bing and Zhang, Lei. A. survey of opinion mining and sentiment analysis. In Mining Text Data, pp. 415–463. Springer, 2012.
[3] Vinodhini G., Chandrasekaran R. M. Sentiment analysis and opinion mining: a survey. International Journal. 2012 Jun;2(6):282-92.
[4] Khan, Khairullah, Baharum B. Baharudin, and Aurangzeb Khan. "Mining opinion from text documents: A survey." In Digital Ecosystems and Technologies, 2009. DEST'09. 3rd IEEE International Conference on, pp. 217-222. IEEE, 2009.
[5] Tang, Jie, Yuan Zhang, Jimeng Sun, Jinhai Rao, Wenjing Yu, Yiran Chen, and Alvis Cheuk M. Fong. "Quantitative study of individual emotional states in social networks." IEEE Transactions on Affective Computing 3, no. 2 (2012): 132-144.
[6] Brynielsson, Joel, Fredrik Johansson, Carl Jonsson, and Anders Westling. "Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises." Security Informatics 3, no. 1 (2014): 7.
[7] H. Çakar and A. Şengür, "Machine Learning Based Emotion Classification Using The Covid-19 Real World Worry Dataset," Anatolian Journal of Computer Sciences, vol. 6, 2021.
[8] J. Wang and L. Wei, "Fear and Hope, Bitter and Sweet: Emotion Sharing of Cancer Community on Twitter," Social Media + Society, vol. 6, no. 1, 2020.
[9] S. Muthana Sarsam, H. Al-Samarraie, A. Ibrahim Alzahrani, W. Alnumay and A. Paul Smith, "A lexicon-based approach to detecting suicide-related messages on Twitter," Biomedical Signal Processing and Control , vol. 65, 2021.
[10] Liu, Bing. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5, no. 1 (2012): 1-167.
[11] A. R. Murthy and A. Kumar K. M., "A Review of Different Approaches for Detecting Emotion from Text," in 240th ECS Meeting, 2021.
[12] Saif, Hassan, Yulan He, and Harith Alani. "Semantic sentiment analysis of twitter." In International semantic web conference, pp. 508-524. Springer, Berlin, Heidelberg, 2012.
[13] De Choudhury, Munmun, Michael Gamon, and Scott Counts. "Happy, nervous or surprised? classification of human affective states in social media." In Sixth International AAAI Conference on Weblogs and Social Media. 2012.
[14] Hasan Maryam, Elke Rundensteiner, and Emmanuel Agu. "Automatic emotion detection in text streams by analyzing Twitter data." International Journal of Data Science and Analytics (2018): 1-17.
[15] Pennington, Jeffrey, Richard Socher, and Christopher Manning. "Glove: Global vectors for word representation." In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543. 2014.
[16] Zamani, Hamed, and W. Bruce Croft. "Relevance-based word embedding." In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 505-514. ACM, 2017.
[17] Kowsari, K., Heidarysafa, M., Brown, D. E., Meimandi, K. J., Barnes, L. E.: Rmdl: Random multimodel deep learning for classi_cation. In: Proceedings of the 2nd In-ternational Conference on Information System and Data Mining. pp. 19{28 (2018).
[18] Kim, Yoon. "Convolutional neural networks for sentence classification." arXiv preprint arXiv:1408.5882 (2014).
[19] Oualil, Youssef, Mittul Singh, Clayton Greenberg, and Dietrich Klakow. "Long-short range context neural networks for language modeling." arXiv preprint arXiv:1708.06555 (2017).
[20] Ekman, Paul. "An argument for basic emotions." Cognition & emotion 6, no. 3-4 (1992): 169-200.
[21] Kim, Sunghwan Mac, Alessandro Valitutti, and Rafael A. Calvo. "Evaluation of unsupervised emotion models to textual affect recognition." In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 62-70. Association for Computational Linguistics, 2010.
[22] Hasan Maryam, Elke Rundensteiner, and Emmanuel Agu. "Emotex: Detecting emotions in twitter messages." (2014).
[23] Canales, Lea, and Patricio Martínez-Barco. "Emotion detection from text: A survey." In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC), pp. 37-43. 2014.
[24] Posner, Jonathan, James A. Russell, and Bradley S. Peterson. "The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology." Development and psychopathology 17, no. 3 (2005): 715-734.
[25] Suttles, Jared, and Nancy Ide. "Distant supervision for emotion classification with discrete binary values." In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 121-136. Springer, Berlin, Heidelberg, 2013.
[26] Plutchik, Robert. "Emotion." A psychoevolutionary synthesis(1980).
[27] Kort, Barry, Rob Reilly, and Rosalind W. Picard. "An affective model of interplay between emotions and learning: Reengineering educational pedagogy-building a learning companion." In Advanced Learning Technologies, 2001. Proceedings. IEEE International Conference on, pp. 43-46. IEEE, 2001.
[28] Constantine, Layale, and Hazem Hajj. "A survey of ground-truth in emotion data annotation." In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2012 IEEE International Conference on, pp. 697-702. IEEE, 2012.
[29] Sosa, Pedro M. "Twitter Sentiment Analysis using combined LSTM-CNN Models." (2017).
[30] Chollet, F., et al.: Keras: Deep learning library for theano and tensorow. https://keras.io/ (2015).
[31] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825{2830 (2011).