Robust Deep Learning Based Sentiment Classification of Code-Mixed Text

Siddhartha Mukherjee, Vinuthkumar Prasan, Anish Nediyanchath, Manan Shah, Nikhil Kumar

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Abstract
India is one of unique countries in the world that has the legacy of diversity of languages. Most of these languages are influenced by English. This causes a large presence of code-mixed text in Social Media. Enormous presence of this code-mixed text provides an important research area for Natural Language Processing (NLP). This paper proposes a novel Attention based deep learning technique for Sentiment Classification on Code-Mixed Text (ACCMT) of Hindi-English. The proposed architecture uses fusion of character and word features. Non availability of suitable Word Embedding to represent these Code-Mixed texts is another important hurdle for this league of NLP tasks. This paper also proposes a novel technique for preparing Word Embedding of Code-Mixed text. This embedding is prepared with two separately trained word-embedding on Romanized Hindi and English respectively. This embedding is further used in the proposed deep learning based architecture for robust classification. The Proposed technique achieves 71.97% accuracy, which exceeds the baseline accuracy.
Anthology ID:
2019.icon-1.14
Volume:
Proceedings of the 16th International Conference on Natural Language Processing
Month:
December
Year:
2019
Address:
International Institute of Information Technology, Hyderabad, India
Editors:
Dipti Misra Sharma, Pushpak Bhattacharya
Venue:
ICON
SIG:
Publisher:
NLP Association of India
Note:
Pages:
124–129
Language:
URL:
https://aclanthology.org/2019.icon-1.14
DOI:
Bibkey:
Cite (ACL):
Siddhartha Mukherjee, Vinuthkumar Prasan, Anish Nediyanchath, Manan Shah, and Nikhil Kumar. 2019. Robust Deep Learning Based Sentiment Classification of Code-Mixed Text. In Proceedings of the 16th International Conference on Natural Language Processing, pages 124–129, International Institute of Information Technology, Hyderabad, India. NLP Association of India.
Cite (Informal):
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text (Mukherjee et al., ICON 2019)
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