The exponential growth of social media data in the era of Web 2.0 has necessitated advanced techniques for sentiment analysis. While sentiment analysis in monolingual datasets has received significant attention that in code-mixed datasets still need to be studied more. Code-mixed data often contain a mixture of monolingual content (might be in transliterated form), single-script but multilingual content, and multi-script multilingual content. This paper explores the issue from three important angles. What will be the best strategy to deal with the data for sentiment detection? Whether to train the classifier with the whole of the dataset or only with the pure code-mixed subset from the dataset? How much important is the language identification (LID) for the task? If LID is to be done, how, and when will it be used to yield the best performance? We explore the questions in the light of three datasets of Tamil–English, Kannada–English, and Malayalam–English YouTube social media comments. Our solution incorporated mBERT and an optional LID module. We report our results using a set of metrics like precision, recall, score, and accuracy. The solutions provide considerable performance gain and some interesting insights for sentiment analysis from code-mixed data.

In the midst of the widespread adoption of technology, particularly among younger generations, the increasing prevalence of hate speech online has become a pressing global concern. This research paper aims to address this urgent issue by conducting a thorough investigation into hate speech detection in Hindi-English code-mixed data. Existing research has largely approached hate speech recognition as a text classification problem, focusing on predicting the class of a message based solely on its textual content. Our task, however, delves into the classification of hateful content disseminated through tweets, comments, and replies on Twitter, taking into account the contextual intricacies inherent in social media communication. In this context, contextual nuances play a crucial role in understanding communication dynamics. By employing state-of-the-art deep learning techniques tailored to the unique linguistic characteristics of each language, this research makes a significant contribution to the development of robust and culturally sensitive hate speech detection systems. Such systems are essential for creating safer online environments and promoting cross-cultural understanding.