Sentiment Analysis Using Deep Learning Approaches on Multi-Domain Dataset in Telugu Language

Kannaiah Chattu () and D. Sumathi
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Kannaiah Chattu: School of Computer Science and Engineering, VIT-AP University Amaravati, India
D. Sumathi: School of Computer Science and Engineering, VIT-AP University Amaravati, India

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 03, 1-28

Abstract: Recent advancements in Natural Language Processing (NLP) have made sentiment analysis an essential component of a variety of NLP jobs, including recommendation systems, question answering, and business intelligence products. While sentiment analysis research has, to put it mildly, been widely pursued in English, Telugu has barely ever attempted the task. The majority of research works concentrate on analysing the sentiments of Tweets, news, or reviews containing Hindi and English words. There is a growing interest among academics in studying how people express their thoughts and views in Indian languages like Bengali, Telugu, Malayalam, Tamil and so on. Due to a paucity of labelled datasets, microscopic investigation on Indian languages has been published to our knowledge. This work suggested a sentence-level sentiment analysis on multi-domain datasets that has been collected in Telugu. Deep learning models have been used in this work because it demonstrates the significant expertise in sentiment analysis and is widely regarded as the cutting-edge model in Telugu Sentiment Analysis. Our proposed work investigates a productive Bidirectional Long Short-Term Memory (BiLSTM) Network and Bidirectional GRU Network (BiGRU) for improving Telugu Sentiment Analysis by encapsulating contextual information from Telugu feature sequences using Forward-Backward encapsulation. Further, the model has been deployed by merging the domains so as to predict the accuracy and other performance metrics. The experimental test findings show that the deep learning models outperform when compared with the baseline traditional ML methods in four benchmark sentiment analysis datasets. There is evidence that the proposed sentiment analysis method has improved precision, recall, F1-score and accuracy in certain cases. The proposed model has achieved the F1-score of 86% for song datasets when compared with the other existing models.

Keywords: BiGRU; BiLSTM; Deep learning; Sentiment analysis; Telugu language (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500187

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