NLP

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.

Hate speech identification in code-mixed text is a challenging but necessary task given the rise of harmful content on social media platforms. The goal of this project was to detect and classify hate speech in code-mixed social media conversations, which often involve multiple languages within a single text.

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.

Code-mixed word-level language identification is crucial for accurately tagging the language of each word in a sentence, especially in multilingual social media text. The primary challenge in this task arises from the mixing of languages within a single sentence, script variations, and the phonetic spelling of words.