Chia-Ling Chang

In the era of rapid development of social networks, user reviews of restaurant review websites have grown rapidly. In order to allow users to quickly grasp the key points of review information on review sites, this paper provides an abstractive multi-text summary method that can automatically generate template-based review summaries based on predefined topics and sentiments. In particular, for each predefined topic and each type of sentiment (positive or negative), this study uses the TextRank algorithm to find the most representative sentences to form a summary. This method allows users to quickly grasp the positive and negative opinions of each important aspect of the restaurant. The previous research on generating abstracts from reviews either did not generate abstracts based on topics, or they were based on topics generated by random models. However, the latter method cannot guarantee that the topics generated by the random model are really the topics that the user needs. For a restaurant review, some topics are indispensable. In order to ensure that abstracts can be generated for these essential topics, our method predefines the topics that must be generated, and then generates abstracts for these topics. In the evaluation, this study compared the template method with the Refresh and Gensim systems based on criteria such as informativeness, clarity, usefulness and likes. The results show that the method proposed in this paper is superior to the other two summary methods.

Product reviews co-written by many Internet reviewers can help consumers make purchase decisions and provide a basis for companies to improve their business strategies. For the company, the most important thing is to understand how the various factors of reviews influence the purchase intention. Therefore, we took this issue as the core and investigated the influence of eWOM on purchase intention based on helpfulness, credibility, information quality and professionalism. We adopted feature filtering algorithms and proposed an ensemble model to integrate these classification results to obtain the most accurate prediction. The empirical evaluation shows that the models based on the four importan aspects of reviews can effectively predict the degree of impact of reviews on purchase intention.