BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations

Sreekavitha Parupalli, Vijjini Anvesh Rao, Radhika Mamidi

Abstract
The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using word-level sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.
Anthology ID:
P18-3014
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
99–104
Language:
URL:
https://aclanthology.org/P18-3014
DOI:
10.18653/v1/P18-3014
Bibkey:
Cite (ACL):
Sreekavitha Parupalli, Vijjini Anvesh Rao, and Radhika Mamidi. 2018. BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations. In Proceedings of ACL 2018, Student Research Workshop, pages 99–104, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations (Parupalli et al., ACL 2018)
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