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.
Cite
CITATION STYLE
Anderson, J. (2019). Sentim at SemEval-2019 task 3: Convolutional neural networks for sentiment in conversations. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 302–306). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s19-2052
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