Sentiment Classification of News Text Data Using Intelligent Model

Shitao Zhang

doi:10.3389/fpsyg.2021.758967

Abstract

Text sentiment classification is a fundamental sub-area in natural language processing. The sentiment classification algorithm is highly domain-dependent. For example, the phrase “traffic jam” expresses negative sentiment in the sentence “I was stuck in a traffic jam on the elevated for 2 h.” But in the domain of transportation, the phrase “traffic jam” in the sentence “Bread and water are essential terms in traffic jams” is without any sentiment. The most common method is to use the domain-specific data samples to classify the text in this domain. However, text sentiment analysis based on machine learning relies on sufficient labeled training data. Aiming at the problem of sentiment classification of news text data with insufficient label news data and the domain adaptation of text sentiment classifiers, an intelligent model, i.e., transfer learning discriminative dictionary learning algorithm (TLDDL) is proposed for cross-domain text sentiment classification. Based on the framework of dictionary learning, the samples from the different domains are projected into a subspace, and a domain-invariant dictionary is built to connect two different domains. To improve the discriminative performance of the proposed algorithm, the discrimination information preserved term and principal component analysis (PCA) term are combined into the objective function. The experiments are performed on three public text datasets. The experimental results show that the proposed algorithm improves the sentiment classification performance of texts in the target domain.

Highlights

News media platforms and various social media platforms produce a large amount of content every day, including a large number of comments generated by network users
The experimental results indicate that under the framework of the dictionary learning algorithm, transfer learning discriminative dictionary learning algorithm (TLDDL) uses the projection technology to reduce the differences between different domains, and builds a domaininvariant dictionary to establish a bridge between the related domains
The results indicate that joint learning of projection technology and dictionary learning is an efficient strategy in cross-domain text classification tasks

Summary

INTRODUCTION

News media platforms and various social media platforms produce a large amount of content every day, including a large number of comments generated by network users. Chen et al (2010) developed a combined feature level and instance level transfer learning algorithm This algorithm combined the samples from different domains by evaluating the classification performance in the target domain. Tang et al (2021) developed a graph domain adversarial transfer network for text sentiment classification This algorithm used a gradient reversal layer to obtain the domain-invariant text features and adopted a projection mechanism to obtain the domain-independent feature representations. Fei et al (2020) developed a deep learning structure-based transfer learning algorithm, which combined the cross-entropy and weighted for word into the recurrent neural network framework. I develop a transfer learning discriminative dictionary learning (TLDDL) algorithm for cross-domain text sentiment classification. For the sample y on the test set, the corresponding sparse coding can be obtained by solving Equation 4, and the classification is performed using the obtained sparse coding

Objective

Experiments on Chinese Corpus

Experiments on English Corpus

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT