Abstract
In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy.- Anthology ID:
- S17-2101
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 616–620
- Language:
- URL:
- https://aclanthology.org/S17-2101
- DOI:
- 10.18653/v1/S17-2101
- Cite (ACL):
- Tzu-Hsuan Yang, Tzu-Hsuan Tseng, and Chia-Ping Chen. 2017. deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 616–620, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter (Yang et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/S17-2101.pdf