A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets

Jacob Soman, Saini and Swaminathan, P and Anandan, R and Kalaivani, K (2018) A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets. International Journal of Engineering & Technology, 7 (2.21). p. 319. ISSN 2227-524X

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Abstract

A comparative review of the challenges encountered in sentiment analysis of Indian regional language tweets vs English language tweets Saini Jacob Soman P Swaminathan R Anandan K Kalaivani

With the developed use of online medium these days for sharing views, sentiments and opinions about products, services, organization and people, micro blogging and social networking sites are acquiring a huge popularity. One of the biggest social media sites namely Twitter is used by several people to share their life events, views and opinion about different areas and concepts. Sentiment analysis is the computational research of reviews, opinions, attitudes, views and peoples’ emotions about different products, services, firms and topics through categorizing them as negative and positive emotions. Sentiment analysis of tweets is a challenging task. This paper makes a critical review on the comparison of the challenges associated with sentiment analysis of Tweets in English Language versus Indian Regional Languages. Five Indian languages namely Tamil, Malayalam, Telugu, Hindi and Bengali have been considered in this research and several challenges associated with the analysis of Twitter sentiments in those languages have been identified and conceptualized in the form of a framework in this research through systematic review.
04 20 2018 319 322 10.14419/ijet.v7i2.21.12394 https://www.sciencepubco.com/index.php/ijet/article/view/12394 https://www.sciencepubco.com/index.php/ijet/article/viewFile/12394/4943 https://www.sciencepubco.com/index.php/ijet/article/viewFile/12394/4943

Item Type: Article
Subjects: Computer Science Engineering > Supervised Learning
Divisions: Computer Science Engineering
Depositing User: Mr IR Admin
Date Deposited: 02 Oct 2024 07:18
Last Modified: 02 Oct 2024 07:18
URI: https://ir.vistas.ac.in/id/eprint/7923

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