HCMS at SemEval-2020 Task 9: A Neural Approach to Sentiment Analysis for Code-Mixed Texts
arXiv:2007.12076v1992 citations
Originality Synthesis-oriented
AI Analysis
This addresses sentiment analysis for code-mixed language users, but it is incremental as it applies existing methods to a specific task.
The paper tackled sentiment classification for Hindi-English code-mixed texts, achieving an F1 score of 67.1% using a neural approach with convolution and attention.
Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.