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

Arik Pamnani, Rajat Goel, Jayesh Choudhari, Mayank Singh

[How to correct problems with metadata yourself]

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://preview.aclanthology.org/teach-a-man-to-fish/S19-2039.pdf
Code
 lingo-iitgn/emocontext-19
Data
EmoContext
PDF Search Code