Sentimental analysis of Indian regional languages on social media
Kakuthota Rakshitha, H M Ramalingam, M Pavithra, H D Advi, Maithri Hegde
Abstract
The idea of sentimental analysis is getting attention for the last few years. The key challenges in a sentimental analysis are the collection of huge data from the sources, applying appropriate algorithms or techniques, and classifying them into different sentiments. In this fast-spreading internet world, social media provides a platform for individuals to express their sentiments. With the changing ways of things in different areas in our day-to-day life, the way of expressing one's view or opinion has also changed. People tend to express themselves in their regional language or in a way convenient to them. These individual reviews play an important role in decision-making. With the huge amount of data that is obtained on social media, it is of no use if the opinions are not classified based on their sentiments. This paper provides information about the tweets posted by the customer are positive, negative, or neutral. For this the proposed model first scrape the tweets from Twitter by using Twitter APIs, then later by using text blob, the customer reviews are given different sentiment scores and classify them as positive, negative, or neutral by using text classification model. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.