Sentiment Analysis in Code-Mixed Telugu-English Text with Unsupervised Data Normalization

Siva Subrahamanyam Varma Kusampudi, Preetham Sathineni, Radhika Mamidi


Abstract
In a multilingual society, people communicate in more than one language, leading to Code-Mixed data. Sentimental analysis on Code-Mixed Telugu-English Text (CMTET) poses unique challenges. The unstructured nature of the Code-Mixed Data is due to the informal language, informal transliterations, and spelling errors. In this paper, we introduce an annotated dataset for Sentiment Analysis in CMTET. Also, we report an accuracy of 80.22% on this dataset using novel unsupervised data normalization with a Multilayer Perceptron (MLP) model. This proposed data normalization technique can be extended to any NLP task involving CMTET. Further, we report an increase of 2.53% accuracy due to this data normalization approach in our best model.
Anthology ID:
2021.ranlp-1.86
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
753–760
Language:
URL:
https://aclanthology.org/2021.ranlp-main.86
DOI:
Bibkey:
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PDF:
https://aclanthology.org/2021.ranlp-main.86.pdf