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Journal of Artificial Intelligence and Capsule Networks

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

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Deniable Authentication Encryption for Privacy Protection using Blockchain
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
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Volume - 3 | Issue - 3 | september 2021

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Pages: 151-168
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.

Keywords

COVID19 social media Twitter sentiment analysis artificial intelligence

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