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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/S19-2039.pdf
- Code
- lingo-iitgn/emocontext-19
- Data
- EmoContext