“Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data
Aman Priyanshu | Aleti Vardhan | Sudarshan Sivakumar | Supriti Vijay | Nipuna Chhabra
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
The increasing use of social media sites in countries like India has given rise to large volumes of code-mixed data. Sentiment analysis of this data can provide integral insights into people’s perspectives and opinions. Code-mixed data is often noisy in nature due to multiple spellings for the same word, lack of definite order of words in a sentence, and random abbreviations. Thus, working with code-mixed data is more challenging than monolingual data. Interpreting a model’s predictions allows us to determine the robustness of the model against different forms of noise. In this paper, we propose a methodology to integrate explainable approaches into code-mixed sentiment analysis. By interpreting the predictions of sentiment analysis models we evaluate how well the model is able to adapt to the implicit noises present in code-mixed data.