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
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/S19-2030.pdf
- Data
- EmoContext