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LSTM Based Emotion Analysis of Text in Tamil Language

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dc.contributor.author Ahamed, M.R. Faiyaz
dc.contributor.author Arachchi, S. P. Kasthuri
dc.date.accessioned 2023-02-17T04:10:43Z
dc.date.available 2023-02-17T04:10:43Z
dc.date.issued 2022
dc.identifier.citation Ahamed M.R. Faiyaz; Arachchi S. P. Kasthuri (2022), LSTM Based Emotion Analysis of Text in Tamil Language, 7th International Conference on Advances in Technology and Computing (ICATC 2022), Faculty of Computing and Technology, University of Kelaniya Sri Lanka. Page 73 – 79. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/25982
dc.description.abstract The sentiments and emotions expressed by users on the internet greatly influence the decision-making process of business firms. Recent studies show that emotion analysis yields more precise information than sentiment analysis. Text emotion analysis has become popular for higher-demand languages like English, Chinese, French, and Arabic. However, no prior studies have been conducted on locally speaking languages, including Tamil, Malayalam, and Sinhala. Therefore, this paper presents a deep learning based novel model to identify the emotions expressed in Tamil texts using a Long Short-Term Memory (LSTM) network. Besides, to enhance the robustness of our proposed model, we conducted experiments with machine learning classifiers, including Support Vector Machine (SVM), Naïve Bayes (NB), Logistic Regression (LR), and Random Forest Classifier (RFC). The experimental results prove that our Tamil text emotion analysis model significantly outperforms other machine learning models, achieving an accuracy of 80%. en_US
dc.publisher Faculty of Computing and Technology, University of Kelaniya Sri Lanka en_US
dc.subject Sentiment Analysis, Emotion Analysis, Machine Learning, Recurrent Neural Network, LSTM en_US
dc.title LSTM Based Emotion Analysis of Text in Tamil Language en_US


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