YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model
Yuanye He, Liang-Chih Yu, Kuo-Hua Robert Lai, Weiyi Liu · 2017
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.