Sentiment analysis method based on an improved modifying‐matrix language model

Abstract

In this paper, a sentence-level sentiment analysis method is proposed to deal with sentiment measurement and classification problems. It is developed from a model called the synthetic and computational language model (SCLM), which represents modifying and modified information, respectively, using matrices and vectors. In the proposed method, a global modifying matrix of a sentence is constructed, the determinant value of this matrix is calculated and adjusted, and then the final value is used as the sentiment value of the sentence. Regression experiment shows that the deviation between the output sentiment and the target sentiment does not exceed a class distance of five classes. The classification experiment shows that the proposed method has improved most of the performance compared to the simplified SCLM and in some cases, such as in 'very positive' class and 'very negative' class, reaches higher precision performance than the baseline method.

WRGAN: Improvement of RelGAN with Wasserstein Loss for Text Generation

“…The data samples generated from the Chinese poetry dataset can be found in Appendix A.3. The Movie Reviews (MR) dataset has two sentiment classes (negative and positive [40,41]). MR has 4503 samples, including 3152 training samples and 1351 testing samples.…”