A Novel Computer-Aided Emotion Recognition of Text Method Based on WordEmbedding and Bi-LSTM
Jia Wei Zheng · 2019
Emotional analysis in literary and artistic art is a current research hotspot, while emotional-based fiction classification is more complicated than other literary forms. Emotional detection is usually done from the perspective of voice or facial images. Most of the existing text-based research uses typical binary or ternary classification methods. Therefore, how to extract information of emotion from the novel text and classify it is still a problem to be solved. In this paper, a Bi-directional Long Short-Term Memory Language Model (BiLSTM-LM) is proposed. The text sequence is divided into six different emotional categories. Word Embedding method is used to encode vocabulary, and the text is represented as word feature representation and character feature representation respectively. The final emotional output is estimated by combining content words and emotional function words. Experimental results show that the training of the model in this paper can quickly converge, and the detection accuracy is up to 64.09%, which is 4 percentage points higher than the current best model, and basically can effectively classify novels.