Abstract
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.
Highlights
The development of the Internet has given users a platform for freely posting online reviews
This paper proposes two deep learning sentiment classification models based on weak tagging information, which are a Bi-directional Long Short-Term Memory (BiLSTM) sentiment classification model based on two-stage training and a sentiment classification model based on denoising of weak tagging data
The deep learning sentiment classification model based on weak tagging information uses weak tagging data for model training while reducing the negative impact of noise samples in weak tagging data on the classification performance of the sentiment classification model, and improves the classification performance of the sentiment classification model
Summary
The development of the Internet has given users a platform for freely posting online reviews. The following two methods are proposed to train sentiment classification models using tagging data and weak tagging data to reduce the negative impact of noise samples in weak tagging data on the model, thereby improving the classification performance of the model. Experiments show that the two methods proposed in the process of introducing weak tagging information to participate in model training can effectively reduce the negative impact of the noise contained in weak tagging information on the sentiment classification model, thereby improving the sentiment classification performance of the model. Reference [21] introduced weak tagging data in training of deep learning models, and achieved excellent classification performance in sentiment classification tasks. While using weak tagging data, this paper proposes two methods to reduce the negative impact of the noise samples contained in weak tagging data on the sentiment classification model, thereby improving the classification performance of the sentiment classification model
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