Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations

Jacob Anderson


Abstract
In this work convolutional neural networks were used in order to determine the sentiment in a conversational setting. This paper’s contributions include a method for handling any sized input and a method for breaking down the conversation into separate parts for easier processing. Finally, clustering was shown to improve results and that such a model for handling sentiment in conversations is both fast and accurate.
Anthology ID:
S19-2052
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:
302–306
Language:
URL:
https://aclanthology.org/S19-2052
DOI:
10.18653/v1/S19-2052
Bibkey:
Cite (ACL):
Jacob Anderson. 2019. Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 302–306, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
Sentim at SemEval-2019 Task 3: Convolutional Neural Networks For Sentiment in Conversations (Anderson, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2052.pdf
Data
EmoContext