Evaluation of text semantic features using latent dirichlet allocation model

Obtaining useful information from mass data on the Internet has been a hot topic in information process research in recent years. For unstructural data like online reviews based on natural languages, it becomes more challenging. Online consumer reviews reflect customers' real experience and opinions on products or services. However, there are short of methods or tools to help potential customers find high-quality and helpful reviews from a large number of reviews. This paper applied the concept and idea of creative computing to solve this problem. Tf-idf, as a traditional method to extract text features, measures the importance of words through word frequency and ignores the semantic information in the text data, while the topic model makes up for this deficiency. This paper proposed to use the vector of reviews allocated by LDA topic model to represent text semantic features. Basing on semantic features of reviews, it calculated cosine similarity between the thumb up reviews and other reviews and thus obtain the simulated helpfulness scores of all reviews. Then, a linear regression was designed to obtain two features, i.e., the syntax and semantic features, and determine the simulated helpfulness scores. The proposed method was validated by collected online tourism reviews of Forbidden City and Mount Huang on three Chinese representative online tourism platforms. The results showed that the proposed method can effectively obtain and thus compare the helpfulness of online reviews in a creative way. © 2020 Totem Publisher, Inc. All rights reserved.