The purpose of sentiment classification is to solve the problem of automatic judgment of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning sentiment classification models focus on algorithm optimization to improve the classification performance of the model, but when the sample data for manually labeling sentiment tendencies is insufficient, the classification performance of the model will be poor. The deep learning sentiment classification model based on weak tagging information, on the one hand, introduces weak tagging information into the training process of the model to reduce the use of manually tagging data. On the other hand, weak tagging information can represent the sentiment tendency of reviews to a certain extent, but it also contains noise, the model reduces the negative impact of the noise in weak tagging information in order to improve the classification performance of the sentiment classification model. The experimental results show that in the sentiment classification task of hotel online reviews, the deep learning sentiment classification model based on weak tagging information has superior classification performance than the traditional deep model without increasing labor cost.

Sentiment classification aims to solve the problem of automatic judgment of sentiment polarity. In the sentiment classification task of text data, such as online reviews, traditional deep learning models are dedicated to algorithm optimization but ignore the characteristics of imbalanced distribution of the number of classified samples and the inclusion of weak tagging information such as ratings and tags. Based on the traditional deep learning model, the method of random oversampling and cost sensitivity is used to increase the contribution of a minority of samples to the model loss function and avoid the model biasing to the majority of samples. The model training is divided into two stages. In the first stage, a large amount of weak tagging data is used to train the model, therefore a model that captures the sentiment semantics of the data is obtained. After that, the model parameters trained in the first stage are used as the initial parameters of the second stage model training, and only a small amount of tagging data is used to continue training the model to reduce the impact of noise, thus reducing the use of manual tagging samples. The experimental results show that the method is considerably better than traditional deep learning models in the sentiment classification task of hotel review data.

The purpose of sentiment classification is to solve the problem of automatic judgment of sentiment tendency. In the sentiment classification task of text data (such as online reviews), the traditional deep learning model focuses on algorithm optimization, but ignores the characteristics of the imbalanced distribution of the number of samples in each classification, which will cause the classification performance of the model to decrease in practical applications. In this paper, the experiment is divided into two stages. In the first stage, samples of minority class in the sample distribution are used to train a sequence generative adversarial nets, so that the sequence generative adversarial nets can learn the features of the samples of minority class in depth. In the second stage, the trained generator of sequence generative adversarial nets is used to generate false samples of minority class and mix them with the original samples to balance the sample distribution. After that, the mixed samples are input into the sentiment classification deep model to complete the model training. Experimental results show that the model has excellent classification performance in comparing a variety of deep learning models based on classic imbalanced learning methods in the sentiment classification task of hotel reviews.