A NOVEL HYBRID MODEL BY USING CONVOLUTIONAL NEURAL NETWORK AND LONG SHORT-TERM MEMORY FOR TEXT SENTIMENT ANALYSIS

Abstract

Machine learning has been widely applied in sentiment analysis. However, natural language has a structure-dependent relationship, relying on a single network for feature extraction, which limits the precision of text sentiment classification. To extract semantic features and perform classification effectively, the convolutional neural network and long short-term memory model (CNN–LSTM) was proposed in the study. The Word2vec method was employed to train the initial word vector. The CNN convolutional layer and the max-pooling layer were introduced to extract the local features of the text. The LSTM module was adopted to capture the long-term dependencies between word sequences. The features extracted with LSTM and CNN were fused, and efficiency was improved using dropout regularization technology. A case study was performed on the movie review dataset to analyze the classification effect utilizing the traditional and proposed methods. Results demonstrate that compared with the single CNN and LSTM models, the evaluation index F1 of this model improves by 1.93% and 0.97%, respectively. Compared with the randomly initialized feature vector, the precision of the model improves 2.74% by the word embedding.The proposed method can receive parallel input of the text information, which reduces the training time of the network model. The mechanism combining CNN and LSTM compensates for the shortcomings of relying only on word embedding for feature extraction, enabling the model to obtain in-depth sentiment feature and effectively-identified sentiment polarity without external knowledge such as dependency syntax analysis. The proposed method provides a specific reference for the sentiment classification of comment texts in social media. Keywords: Sentiment analysis, CNN, LSTM, Feature fusion