In order to advance a firm, it is crucial to understand user opinions on social media. India has diversity, with Kannada being one of the widely spoken languages. Sentiment analysis in Kannada offers a tool to assess opinion, gather customer feedback, and identify social media trends among the Kannada-speaking community. This kind of analysis assists businesses in comprehending the sentiments expressed in Kannada language customer reviews, social media posts, and online conversations. It empowers them to make choices based on data and customize their offerings to better suit the needs of their customers. This work proposes a model to perform sentiment analysis in Kannada language with four emotions, namely anger, fear, joy, and sadness, using machine learning algorithms like linear support vector classification, logistic regression, stochastic gradient descent, K-nearest neighbors, multinomial naive bayes, and random forest classification. The model achieved an accuracy of 87.25% with a linear support vector classifier.