Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network

Author:

Zhang Yuxiang1,Fu Jiamei12,She Dongyu2,Zhang Ying2,Wang Senzhang3,Yang Jufeng2

Affiliation:

1. College of Computer Science and Technology, Civil Aviation University of China, Tianjin, China

2. College of Computer and Control Engineering, Nankai University, Tianjin, China

3. College of Comp. Sci.&Tech., Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.

Publisher

International Joint Conferences on Artificial Intelligence Organization

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3. Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects;Expert Systems with Applications;2024-03

4. Sentiment Analysis of Social Network Comment Text Based on LSTM and Bert;Journal of Circuits, Systems and Computers;2023-06-12

5. A Topic-Enhanced Approach for Emotion Distribution Forecasting in Conversations;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04

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