Sentiment Analysis of Kannada Political Tweets using Support Vector Machines

  • Shankar R, Suma Swamy, Pranav Raikote
Keywords: kannada sentiment analysis, kannada opinion mining, kannada tweets analysis, support vector machine


Sentiment analysis is a process of computationally identifying and categorizing opinions from a piece of text and determine whether the writer’s attitude towards a particular topic or the product is positive, negative or neutral. Understanding the sentiments or emotions has become important in the current world as it can be proved as the game-changer in improving the user experience over a wide variety of applications. Looking from a business point of view, many industries rely completely on the opinions of the users for their product improvisations. This analysis when manually being done becomes tedious and time consuming. So, by applying Machine Learning algorithms, this can be done with ease and with incredibly good accuracy. Much of the work has been done for the opinions or reviews which is in English language. India is predominantly considered as a country with wide variety of regional languages like Kannada, Telugu, Tamil etc. Understanding the sentiments of languages apart from English is greatly beneficial in reaching the local people. In this article, the texts of Kannada Tweets collected from various political parties and politicians are analyzed to classify the tweets as positive or negative or neutral interpretations. These interpretations helps a political party or politicians to improve the           worthiness. Support vector machines which is a supervised algorithm has been used to work on a set of hyper planes resulting in a better classification

How to Cite
Pranav Raikote, S. R. S. S. (2021). Sentiment Analysis of Kannada Political Tweets using Support Vector Machines. Design Engineering, 647-656. Retrieved from