Contextual-cognitive convolutional attentive LSTM approach for sentiment analysis of Tamil review

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M. Kavitha
Dr. A. John Sanjeev Kumar


Movie goers use Twitter and other websites to share their thoughts and feelings regarding Tamil films in this age of social networking and online interaction. It is essential to examine these feelings for realizing the responses of the public and assess a film's level of success. In this research, a novel contextual-cognitive convolutional attentive long/short-term memory (C3-ALSTM) strategy for assessing Tamil movie reviews via Tamil tweets is presented. We first gather the Tamil movie review dataset through an open database to assess the suggested C3-ALSTM approach. We normalize the gathered data by eliminating extraneous information, noise and redundant data. Tokenization and the removal of stop words and special characters are employed in this phase. Following data cleansing, term frequency-inverse document frequency (TF-IDF) is applied for extracting the attributes. The suggested approach evaluates the Tamil reviews based on these retrieved features. The suggested approach is put into practice using the Python platform and its accuracy, precision, recall and f-measure metrics are examined. According to the research's findings, the suggested method outperforms other used techniques for sentiment assessment of Tamil reviews. In addition, this research attempts to offer insightful information about how the public reacts to particular Tamil films, advancing knowledge of public preferences and attitudes toward the Tamil cinema industry.


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How to Cite
Kavitha, M., & Kumar, D. A. J. S. . (2020). Contextual-cognitive convolutional attentive LSTM approach for sentiment analysis of Tamil review. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 2493–2504.
Research Articles


. Zunic, A., Corcoran, P. and Spasic, I., 2020. Sentiment analysis in health and well-being: systematic review. JMIR medical informatics, 8(1), p.e16023.

. Anandhan, A., Shuib, L., Ismail, M.A. and Mujtaba, G., 2018. Social media recommender systems: review and open research issues. IEEE Access, 6, pp.15608-15628.

. Krishnan, I.A., Ching, H.S., Ramalingam, S., Maruthai, E., Kandasamy, P., De Mello, G., Munian, S. and Ling, W.W., 2020. Challenges of learning English in the 21st century: Online vs. traditional during Covid-19. Malaysian Journal of Social Sciences and Humanities (MJSSH), 5(9), pp.1-15.

. Sima, V., Gheorghe, I.G., Subić, J. and Nancu, D., 2020. A systematic review of the influences of the Industry 4.0 revolution on human capital development and consumer behavior. Sustainability, 12(10), p.4035.

. Gutiérrez-Martín, A. and Torrego-González, A., 2018. The Twitter games: media education, popular culture and multiscreen viewing in virtual concourses. Information, Communication & Society, 21(3), pp.434-447.

. Reyes-Menendez, A., Saura, J.R. and Alvarez-Alonso, C., 2018. Understanding# WorldEnvironmentDay user opinions in Twitter: A topic-based sentiment analysis approach. International journal of environmental research and public health, 15(11), p.2537.

. Durriyah, T.L. and Zuhdi, M., 2018. Digital Literacy with EFL Student Teachers: Exploring Indonesian Student Teachers' Initial Perception about Integrating Digital Technologies into a Teaching Unit. International Journal of Education and Literacy Studies, 6(3), pp.53-60.

. Dreisbach, C., Koleck, T.A., Bourne, P.E. and Bakken, S., 2019. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International journal of medical informatics, 125, pp.37-46.

. Nag, S. and Narayanan, B., 2019. Orthographic knowledge, reading, and spelling development in Tamil: The first three years. Handbook of literacy in Akshara orthography, pp.55-83.

. Boruah, D.M., 2020. Language Loss and Revitalization of Gondi language: An Endangered Language of Central India. Language in India, 20(9).

. Thavareesan, S. and Mahesan, S., 2019, December. Sentiment analysis in Tamil texts: A study on machine learning techniques and feature representation. In 2019 14th Conference on Industrial and Information Systems (ICIIS) (pp. 320-325). IEEE.

. Rani, S. and Kumar, P., 2019. A journey of Indian languages over sentiment analysis: a systematic review. Artificial Intelligence Review, 52, pp.1415-1462.

. Ahmad, G.I., Singla, J. and Nikita, N., 2019, April. Review on sentiment analysis of Indian languages with a special focus on code mixed Indian languages. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 352-356). IEEE.

. Soman, S.J., Swaminathan, P., Anandan, R. and Kalaivani, K., 2018. A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets. International Journal of Engineering & Technology, 7(2), pp.319-322.

. Shelke, M.B. and Deshmukh, S.N., 2020. Recent advances in sentiment analysis of Indian languages. International Journal of Future Generation Communication and Networking, 13(4), pp.1656-1675.

. Bhargava, R., Arora, S. and Sharma, Y., 2019. Neural network-based architecture for sentiment analysis in Indian languages. Journal of Intelligent Systems, 28(3), pp.361-375.

. Lakshmi Devi, B., Varaswathi Bai, V., Ramasubbareddy, S. and Govinda, K., 2020. Sentiment analysis on movie reviews. In Emerging Research in Data Engineering Systems and Computer Communications: Proceedings of CCODE 2019 (pp. 321-328). Springer Singapore.

. Ravishankar, N. and Shriram, R., 2018. Grammar rule-based sentiment categorization model for classification of Tamil tweets. International Journal of Intelligent Systems Technologies and Applications, 17(1-2), pp.89-97.

. Shelke, M.B. and Deshmukh, S.N., 2020. Recent advances in sentiment analysis of Indian languages. International Journal of Future Generation Communication and Networking, 13(4), pp.1656-1675.

. Sunitha, P.B., Joseph, S. and Akhil, P.V., 2019, October. A study on the performance of supervised algorithms for classification in sentiment analysis. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON) (pp. 1351-1356). IEEE.