Abstract
This study aims to determine the public sentiment on the revocation of Article 370 by the Indian Government. Article 370 gave special privileges to the Indian state of Jammu and Kashmir, allowing it to have a separate constitution and autonomous internal administration. The textual data for arbitrating this sentiment is extracted from Twitter—a social media platform for sharing content, in the form of tweets, which include the geographical location of the users posting them. With over 96,000 tweets obtained in a span of three weeks, this study finds that the location of the individual influences their opinion toward this revocation. This paper uses three neural network architectures, namely LSTM, bidirectional LSTM, and CNN-LSTM, where the CNN-LSTM model is used to classify the sentiments since it achieves the highest test-set accuracy.
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Mishra, A., Arya, A., Devanand, H.R. (2021). Location-Based Sentiment Analysis of the Revocation of Article 370 Using Various Recurrent Neural Networks. In: Garg, L., Sharma, H., Goyal, S.B., Singh, A. (eds) Proceedings of International Conference on Innovations in Information and Communication Technologies. ICI2CT 2020. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0873-5_4
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DOI: https://doi.org/10.1007/978-981-16-0873-5_4
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