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Journal of Artificial Intelligence and Capsule Networks
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Volume - 3 | Issue - 3 | september 2021

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Milan Tripathi   96  36
151-168
Cite this article
Tripathi, M. (2021). Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM. Journal of Artificial Intelligence and Capsule Networks, 3(3), 151-168. doi:10.36548/jaicn.2021.3.001
Published
23 July, 2021
Abstract

The government's months-long total lockdown in response to the COVID19 outbreak has resulted in a lack of physical connection with others. This resulted in a massive increase in social media communication. Twitter has become one of the most popular places for people to communicate their thoughts and opinions. As a result, massive amounts of data are created every day. These data can assist businesses in making better judgments. In the case of Nepal, there has been relatively little investigation into the text's analysis. Because few researchers are working in the field, development is slow. In this study, Four language-based models for sentiment analysis of Nepali covid19 tweets are designed and evaluated. Because the number of individuals using social media is expected to skyrocket in the next few days, companies will benefit from an AI-based sentiment analysis system. It will greatly assist firms in adapting to the changing climate.

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