Abstract: With the rapid development of the mobile Internet, social media platforms and electronic commerce, a large amount of online reviews with user comments have been generated and show great business value. The recognition of usefulness of user comments provides an important guarantee for mining valuable information in comments. To this end, a classification algorithm is proposed based on the Knowledge Adoption Model (KAM) theory and Multilayer Perceptron (MLP) neural networks to identify the usefulness of online reviews in social media platforms. According to the knowledge adoption model theory, the algorithm extracts the featuresfor identifying usefulness of comments from two aspects: review quality and review source credibility.Specifically, to represent the review quality by constructing featuresusing the proportion of domain words and the proportion of stop words via a dictionary containing prior domain knowledge, which effectively alleviates the problem of domain knowledge barriers in cross-domain reviews; to represent the review source credibility by constructing the features based on the number of fans of the author and the number of likes the author has already received so far. In order to verify the recognition effect of the method proposed, traditional Chinese medicine related comments are crawled from Zhihu.com as the experimental data set. The experimental results show that the feature construction method, which integrates domain knowledge into text feature representation by calculating the proportion of domain words, can effectively alleviates the problems of knowledge barriers in specific fields; and the proposed method provides important information of review quality for the identification of the usefulness of online reviews, and thus has improved the identifying effect of usefulness of online comments and increasedthe interpretability to some extent.

Key words: online reviews; usefulness of online reviews; knowledge adoption model; multilayer perceptron neural network