Text sentiment analysis is one of the most important tasks in the field of public opinion monitoring, service evaluation and satisfaction analysis in the current network environment. At present, the sentiment analysis algorithms with good effects are all based on statistical learning methods. The performance of this method depends on the quality of feature extraction, while good feature engineering requires a high degree of expertise and is also time-consuming, laborious, and affords poor opportunities for mobility. Neural networks can reduce dependence on feature engineering. Recurrent neural networks can obtain context information but the order of words will lead to bias; the text analysis method based on convolutional neural network can obtain important features of text through pooling but it is difficult to obtain contextual information. Aiming at the above problems, this paper proposes a sentiment analysis method based on the combination of R-CNN and C-RNN based on a fusion gate. Firstly, RNN and CNN are combined in different ways to alleviate the shortcomings of the two, and the sub-analysis network R-CNN and C-RNN finally combine the two networks through the gating unit to form the final analysis model. We performed experiments on different data sets to verify the effectiveness of the method.
TEXT SENTIMENT ANALYSIS BASED ON CNNS AND SVM
