IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning

Arik Pamnani, Rajat Goel, Jayesh Choudhari, Mayank Singh


Abstract
Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of these are text-based messaging platforms. Development of Chatbots that automatically understand latent emotions in the textual message is a challenging task. In this paper, we present an automatic emotion detection system that aims to detect the emotion of a person textually conversing with a chatbot. We explore deep learning techniques such as CNN and LSTM based neural networks and outperformed the baseline score by 14%. The trained model and code are kept in public domain.
Anthology ID:
S19-2039
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
236–240
Language:
URL:
https://aclanthology.org/S19-2039
DOI:
10.18653/v1/S19-2039
Bibkey:
Cite (ACL):
Arik Pamnani, Rajat Goel, Jayesh Choudhari, and Mayank Singh. 2019. IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 236–240, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning (Pamnani et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2039.pdf
Code
 lingo-iitgn/emocontext-19
Data
EmoContext