Sentiment classification based on weak tagging information and imbalanced data

Abstract

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.