Sentiment Classification of News Text Data Using Intelligent Model

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.