Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance.

In order to solve the problem of data sparsity and credibility in collaborative filtering, a recommendation system based on sentiment analysis and matrix factorization (SAMF) is proposed in this paper, which uses topic model and deep learning technology to fully mine the implicit information in reviews to improve the rating matrix and assist recommendation. Firstly, user topic distribution and item topic distribution are generated from reviews(consisting user reviews and item reviews) through LDA(Latent Dirichlet Allocation). The user feature matrix and item feature matrix are created based on topic probability. Secondly, user feature matrix and item feature matrix are integrated to create user-item preference matrix. Thirdly, the user-item preference matrix and the original rating matrix are integrated to create the user-item rating matrix. Fourthly, BERT(Bidirectional Encoder Representation from Transformers) is used to quantify the sentiment information contained in the reviews and integrate the sentiment information with the user-item rating matrix, to modify and update the user-item rating matrix. Finally, the updated user-item rating matrix is used to achieve rating prediction and Top-N recommendation. Experiments on Amazon datasets demonstrates that the proposed SAMF has better recommendation performance than other classical algorithms.