SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection

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

Abstract
The system developed by the SSN MLRG1 team for Semeval-2018 task 1 on affect in tweets uses rule based feature selection and one-hot encoding to generate the input feature vector. Multilayer Perceptron was used to build the model for emotion intensity ordinal classification, sentiment analysis ordinal classification and emotion classfication subtasks. Support Vector Machine was used to build the model for emotion intensity regression and sentiment intensity regression subtasks.
Anthology ID:
S18-1048
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:
324–328
Language:
URL:
https://aclanthology.org/S18-1048
DOI:
10.18653/v1/S18-1048
Bibkey:
Cite (ACL):
Angel Deborah S, Rajalakshmi S, S Milton Rajendram, and Mirnalinee T T. 2018. SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 324–328, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
SSN MLRG1 at SemEval-2018 Task 1: Emotion and Sentiment Intensity Detection Using Rule Based Feature Selection (S et al., SemEval 2018)
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PDF:
https://preview.aclanthology.org/improve-issue-templates/S18-1048.pdf
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