@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://preview.aclanthology.org/iwcs-25-ingestion/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."
}