Sentiment Analysis on Indian Indigenous Languages: A Review on Multilingual Opinion Mining

Sonali Rajesh Shah; Abhishek Kaushik

doi:10.20944/preprints201911.0338.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 26 November 2019 / Approved: 27 November 2019 / Online: 27 November 2019 (09:30:07 CET)

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

Indian; Sentiment Analysis; Indigenous Languages; Machine Learning; Deep learning; Data; Opinion Mining; Languages.

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Sentiment Analysis on Indian Indigenous Languages: A Review on Multilingual Opinion Mining

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