Extracting Emotion-Cause Pairs Based on Emotional Dilation Gated CNN
Dai Jianhua1,2,3(),Deng Yubin1,3
1Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China 2Research Institute of Languages and Cultures, Hunan Normal University, Changsha 410081, China 3College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
[Objective] This paper proposes an Emotional Dilation Gated CNN (EDGCNN) model, aiming to extract emotion-cause pairs for sentiment analysis. [Methods] First, we used the emotional discriminant model to identify sentiment sentences. Then, we input coding for these sentences to the EDGCNN model and located corresponding reasons. Finally, we tagged keywords of reasons generated from the experimental dataset. [Results] The new model’s recall and F1 values reached 63.52% and 60.45% respectively on the training dataset, which were better or very similiar to the existing ones The proposed model also extracted emotion-cause pairs at finergranularity level. [Limitations] The experimental corpus size was small. [Conclusions] The proposed model can extract emotion-cause pairs effectively.
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Dai Jianhua, Deng Yubin. Extracting Emotion-Cause Pairs Based on Emotional Dilation Gated CNN. Data Analysis and Knowledge Discovery, 2020, 4(8): 98-106.
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