E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text

Harsh Patel


Abstract
This paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is ”Contextual Emotion Detection in Texts”. The paper includes the explanation of an architecture that I created by exploiting the embedding layers of Word2Vec and GloVe using LSTMs as memory unit cells which detects approximate emotion of chats between two people in the English language provided in the textual form. The set of emotions on which the model was trained was Happy, Sad, Angry and Others. The paper also includes an analysis of different conventional machine learning algorithms in comparison to E-LSTM.
Anthology ID:
S19-2030
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:
190–194
Language:
URL:
https://aclanthology.org/S19-2030
DOI:
10.18653/v1/S19-2030
Bibkey:
Cite (ACL):
Harsh Patel. 2019. E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 190–194, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
E-LSTM at SemEval-2019 Task 3: Semantic and Sentimental Features Retention for Emotion Detection in Text (Patel, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2030.pdf
Data
EmoContext