Comparative study of machine learning algorithms for Kannada twitter sentimental analysis

Analyzing the client’s reviews from various online platform helps to improvise the business to higher levels. These User’s opinions can be analyzed using Sentiment Analysis. Sentimental analysis on Indian languages is a tedious work as there is a wide diversity in different languages of the India. Kannada is one of the prominent languages in India as 43 million of Indian population use Kannada as their native language for communication and it holds 27th rank among top 30 languages across the world, as there is very less work carried out on Indian languages, especially in Kannada language, more work is required to process the Kannada language across different domains. The sentimental analysis on the Kannada language has the accuracy about 72% from the previous work. So, in this work, we have made comparative study of various machine learning algorithms for Kannada Twitter sentimental analysis. It is experimented on live Twitter data and found that Multinomial Naive Bayes Classifier has performed better with accuracy of 75%.