International Core Journal of Engineering 2020-26 | Page 198

2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) A Novel Computer-Aided Emotion Recognition of Text Method Based on WordEmbedding and Bi- LSTM Jia Zheng The Harker School, San Jose, State of California 95112-7400, America. Corresponding author: [email protected] detection and multiple types of facial expression classification based on support vector machine (SVM), and used LBP histogram to obtain facial feature vector with dual form. Literature [7] proposed a facial expression algorithm based on convolutional neural network, which uses EEG signals for classification. For example, literature [8] proposed a convolutional neural network speech emotion recognition based on recursive neural network structure, and used 13 MFCC (mere frequency cestrum coefficient) of speech signals as classification features. Abstract—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. There is little research on extracting information of emotion from text input. The literature [9] developed a semantic network that extracts emotions from text content, but the corpus supporting the results is rare. With the advent of the era of big data, the scale of text data is also constantly expanding. The deep learning method shows its advantages. It is driven by data and can automatically obtain useful feature knowledge from data without manual participation, which avoid a lot of prior knowledge. Literature [10] proposed a student feedback text academic emotion recognition method based on LSTM model. Although the recognition rate can reach 90%, it only uses binary classification, namely positive emotion and negative emotion. Literature [11] identified the basic six emotions by analyzing the text, namely happiness, sadness, fear, surprise, anger, and jealousy. However, the recognition rate is very low. Keywords—Novel Classification, Emotion Detection, Language Model; Bi-directional Long Short-Term Memory Network. I. I NTRODUCTION The transfer of emotion is an important role of literature and art. As a member of a large family of literature, novels also have the common character of literature - emotion. It is a current research hotspot to classify novels according to emotion. According to the emotional model theory [1], all emotions can be divided into 3 categories, 6 groups and 22 types. Although there are many emotional theories, from literature [2] study it is found that human beings have six basic emotions, namely, happiness, sadness, fear, surprise, anger and jealousy. Other emotions are combined with these six basic emotions and belong to compound emotions. For the text emotion analysis problem, this paper proposes a Bi-directional Long Short-Term Memory (Bi- LSTM), using a Language Model (LM), which is a BiLSTM-LM. LSTM solves the problem of gradient disappearance of RNN through a gate mechanism and can effectively learn long-term relationships of dependency. Combined with the language model, it acquires many linguistic features of data, accelerates the convergence speed of training, and improves the generalization ability of the model. Through the comparative analysis of experiments, the BiLSTM-LM model proposed in this paper has significantly improved compared with other deep learning models for the recognition of six basic emotions. The research on emotion focuses on the mechanism of emotion production, such as affecting factors of emotion, emotional classification, emotional impact on people, while the research on the judgment of human emotion is relatively less [3]. Existing studies on emotional judgment are all about expression, micro-expression and physical behavior, which through the observation of other people's expressions and behaviors, judge their emotions [4]. II. B ASIC T HEORY A. Long Short-Term Memory Long Short-Term Memory is a time recursive neural network, which is suitable for processing and predicting important events with relatively long interval and delay in time series. LSTM is proposed to solve the problem of “gradient disappearance” in the Recurrent Neural Network (RNN) structure of RNN, which is a special kind of At present, the research of emotion recognition technology mainly focuses on three aspects: image, voice and text. Literature [5] resolved the problem of classification of smiles and neutral faces by using power LBP features. Literature [6] proposed two types of emotion 978-1-7281-4691-1/19/$31.00 ©2019 IEEE DOI 10.1109/AIAM48774.2019.00042 176