Abstract
In this paper, we present a system that uses a convolutional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has two combined parts: CNN extracts local n-gram features within tweets and LSTM composes the features to capture long-distance dependency across tweets. Additionally, we used other three models (CNN, LSTM, BiLSTM) as baseline algorithms. Our introduced model showed good performance in the experimental results.- Anthology ID:
- W17-5227
- Volume:
- Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 200–204
- Language:
- URL:
- https://aclanthology.org/W17-5227
- DOI:
- 10.18653/v1/W17-5227
- Cite (ACL):
- You Zhang, Hang Yuan, Jin Wang, and Xuejie Zhang. 2017. YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 200–204, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction (Zhang et al., WASSA 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-5227.pdf