SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron

Rajalakshmi S, Angel Deborah S, S Milton Rajendram, Mirnalinee T T

Abstract
Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.
Anthology ID:
S18-1103
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
633–637
Language:
URL:
https://aclanthology.org/S18-1103
DOI:
10.18653/v1/S18-1103
Bibkey:
Cite (ACL):
Rajalakshmi S, Angel Deborah S, S Milton Rajendram, and Mirnalinee T T. 2018. SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 633–637, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron (S et al., SemEval 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-3/S18-1103.pdf
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