AICLLGSep 7, 2021

ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models

arXiv:2109.03200v2
Originality Synthesis-oriented
AI Analysis

This addresses the need for interpretable sentiment analysis in multilingual contexts like India, but appears incremental as it builds on existing BERT models.

The paper tackled sentiment analysis on code-mixed data from social media by integrating explainable approaches, but no concrete results or numbers were provided in the 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. Developing robust explainability techniques which explain why models make their predictions becomes essential. In this paper, we propose an adequate methodology to integrate explainable approaches into code-mixed sentiment analysis.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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