YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model Article Swipe
Related Concepts
Computer science
Task (project management)
Artificial intelligence
Pearson product-moment correlation coefficient
Sentiment analysis
Correlation coefficient
Process (computing)
Correlation
Natural language processing
Pattern recognition (psychology)
Machine learning
Mathematics
Statistics
Operating system
Economics
Management
Geometry
Yuanye He
,
Liang-Chih Yu
,
K. Robert Lai
,
Weiyi Liu
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.18653/v1/w17-5233
· OA: W2759645571
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.18653/v1/w17-5233
· OA: W2759645571
The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.
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