%0 Conference Proceedings %T “Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data %A Priyanshu, Aman %A Vardhan, Aleti %A Sivakumar, Sudarshan %A Vijay, Supriti %A Chhabra, Nipuna %Y Xu, Wei %Y Ritter, Alan %Y Baldwin, Tim %Y Rahimi, Afshin %S Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021) %D 2021 %8 November %I Association for Computational Linguistics %C Online %F priyanshu-etal-2021-something %X 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. %R 10.18653/v1/2021.wnut-1.48 %U https://aclanthology.org/2021.wnut-1.48 %U https://doi.org/10.18653/v1/2021.wnut-1.48 %P 437-444