International Core Journal of Engineering 2020-26 | Page 201

character feature information of text, a two-channel LSTM neural network model is designed in this paper. The model integrates word feature information and character feature information of text, and can learn feature representation of text in a deeper layer. The structure of the model is shown in Figure 7. Firstly, WordEmbedding is used to encode vocabulary, and text is represented as word feature representation and character feature representation respectively. Then bi- directional LSTM is used to learn higher-dimensional hidden features from two sets of feature representation. h w h c can randomly disable some hidden nodes in the network during model training. Finally, the model completes the task of emotional recognition of text through Sigmoid layer. The Sigmoid layer accepts the output of the previous layer as input, and outputs a one-dimensional vector of length 1. The value of this vector is mapped between 0 and 1 by Sigmoid function, which is used as the model to predict probability of whether the text contains certain emotions. IV. A NALYSIS OF E XPERIMENTAL R ESULTS In the experiment, TensorFlow deep learning framework is used for development and testing, and CUDA is used for parallel acceleration of GPU training process. The configuration parameters are shown in Table I. LSTM w ( T w ) (2) LSTM c ( T c ) Among them, h w and h c represent the features of word hidden layer and character hidden layer learned by LSTM respectively. In order to get the character fusion feature of text, h w and h c are stitched and integrated together. h w c TABLE I. T HE E XPERIMENTAL P LATFORM P ARAMETER C ONFIGURATION . Configuration GPU CPU RAM Operating System Development Platform Development Language Framework h w † h c (3) Among them h w c is the word fusion feature of the text, and the symbol † represents the stitching of vectors. Parameter NVIDIA GeForce GTX 1080Ti 8GB GDDR5X Intel(R) Core (TM) i7-8700 [email protected] 4.6GHz 64GB DDR4 2133MHz Linux Ubuntu 14.0 Anaconda3 Python 3.5 TensorFlow 10.0 The network model shown in figure 7 was used to train 37617 texts and identify the six basic emotions-happiness, sadness, fear, surprise, anger and jealousy. The curve of the loss and recognition accuracy during training along with the epoch was shown in Figure 8 and Figure 9. Next is a full connectivity layer for learning deeper representations of character-word fusion features. In order to reduce the complexity of the model and prevent network from over-fitting of training samples, Dropout layer is added on top of the full connection layer. The Dropout layer Figure 9. Training Accuracy Curve. Figure 8. Training Loss Curve. As can be seen from the figures, for the verification set, it is possible to achieve a stable state of loss with very few epochs. However, the recognition rate remained consistent with the change curve of epoch and loss, and remained stable after the two epochs, with the recognition rate reaching 64%, which is a certain improvement compared with the current 60% of the highest emotional recognition of text, and can be rapidly converged. emotional output. The experimental results show that the multi-classification detection accuracy of the model in this paper is up to 64.09%, and has a fast convergence rate, which can effectively classify novels according to the six basic emotional characteristics. A CKNOWLEDGMENTS This work was financially supported by Fablab. V. C ONCLUSION R EFERENCES This paper proposes a Bi-directional Long Short-Term Memory Language Model for novel classification task based on text emotion recognition. In this paper, text is presented as word feature presentation and character feature presentation respectively by using Word Embedding method to number vocabulary. Content words and emotional function words are integrated to estimate the final [1] [2] [3] [4] 179 J. Cahn, “Generating Expression in Synthesized Speech,” Master’s thesis, MIT, 1989. Keikichi Hirose, Nobuaki Minematsu, etc, “Analytical and perceptual study on the role of acoustic features in realizing emotional speech”, ICSLP2000. Donna Erickson,1 Arthur Abramson, “Articulatory characteristics of emotional utterances in spoken English”, ICSLP2000. Schröder M1, Cowie R2, Douglas-Cowie E2, “Acoustic Correlates of