Sentiment Analysis On Indian Indigenous Languages: A Review On Multilingual Opinion Mining
It addresses the problem of analyzing unstructured sentiment data in under-researched languages for researchers and practitioners in multilingual opinion mining, but it is incremental as it reviews existing work.
This paper reviews approaches, algorithms, and challenges in sentiment analysis for Indian indigenous languages, highlighting the gap in research compared to English and the need to analyze multilingual web data.
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.