@inproceedings{pamnani-etal-2019-iit,
title = "{IIT} {G}andhinagar at {S}em{E}val-2019 Task 3: Contextual Emotion Detection Using Deep Learning",
author = "Pamnani, Arik and
Goel, Rajat and
Choudhari, Jayesh and
Singh, Mayank",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2039",
doi = "10.18653/v1/S19-2039",
pages = "236--240",
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.",
}
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%0 Conference Proceedings
%T IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning
%A Pamnani, Arik
%A Goel, Rajat
%A Choudhari, Jayesh
%A Singh, Mayank
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F pamnani-etal-2019-iit
%X 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.
%R 10.18653/v1/S19-2039
%U https://aclanthology.org/S19-2039
%U https://doi.org/10.18653/v1/S19-2039
%P 236-240
Markdown (Informal)
[IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning](https://aclanthology.org/S19-2039) (Pamnani et al., SemEval 2019)
ACL