Kannada-English

Sentiment analysis on code-mixed text is essential for understanding user opinions, particularly in regions where people frequently switch between languages in their conversations. Traditional sentiment analysis models face difficulty in handling such data due to the complexity introduced by the mixture of languages and the lack of large labeled datasets.

Sarcasm detection in code-mixed text is a particularly challenging task because sarcasm relies heavily on context, tone, and often spans multiple languages within a single sentence. This project aimed to develop a model that could accurately detect sarcasm in code-mixed social media text.