De-Mixing Sentiment from Code-Mixed Text

Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, Philipp Koehn

Abstract
Code-mixing is the phenomenon of mixing the vocabulary and syntax of multiple languages in the same sentence. It is an increasingly common occurrence in today’s multilingual society and poses a big challenge when encountered in different downstream tasks. In this paper, we present a hybrid architecture for the task of Sentiment Analysis of English-Hindi code-mixed data. Our method consists of three components, each seeking to alleviate different issues. We first generate subword level representations for the sentences using a CNN architecture. The generated representations are used as inputs to a Dual Encoder Network which consists of two different BiLSTMs - the Collective and Specific Encoder. The Collective Encoder captures the overall sentiment of the sentence, while the Specific Encoder utilizes an attention mechanism in order to focus on individual sentiment-bearing sub-words. This, combined with a Feature Network consisting of orthographic features and specially trained word embeddings, achieves state-of-the-art results - 83.54% accuracy and 0.827 F1 score - on a benchmark dataset.
Anthology ID:
P19-2052
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
371–377
Language:
URL:
https://aclanthology.org/P19-2052
DOI:
10.18653/v1/P19-2052
Bibkey:
Cite (ACL):
Yash Kumar Lal, Vaibhav Kumar, Mrinal Dhar, Manish Shrivastava, and Philipp Koehn. 2019. De-Mixing Sentiment from Code-Mixed Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 371–377, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
De-Mixing Sentiment from Code-Mixed Text (Lal et al., ACL 2019)
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PDF:
https://preview.aclanthology.org/remove-xml-comments/P19-2052.pdf
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