@inproceedings{priyanshu-etal-2021-something,
title = "{``}Something Something Hota Hai!{''} An Explainable Approach towards Sentiment Analysis on {I}ndian Code-Mixed Data",
author = "Priyanshu, Aman and
Vardhan, Aleti and
Sivakumar, Sudarshan and
Vijay, Supriti and
Chhabra, Nipuna",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)",
month = nov,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wnut-1.48",
doi = "10.18653/v1/2021.wnut-1.48",
pages = "437--444",
abstract = "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.",
}
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<abstract>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.</abstract>
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%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
Markdown (Informal)
[“Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data](https://aclanthology.org/2021.wnut-1.48) (Priyanshu et al., WNUT 2021)
ACL