An Approach to Summarizing Product Reviews

Abstract

Product reviews are an important feature of e-commerce because they influence the communicative behavior of a potential addressee (customers). Users often read online product reviews to get either general or specific information about a product or service. Besides, companies analyze customer reviews to improve their product quality or to adjust their marketing strategy. However, many reviews are unstructured and long. With the number of product reviews growing rapidly, reading a large number of reviews becomes a time-consuming process for both users and companies. Therefore, review summarization becomes a serious issue. In this paper, we propose a new approach to summarizing reviews of electronics and household appliances. The proposed approach makes it possible to structure information for every aspect category; it provides calculation sentiment score for each aspect category, and shows the most relevant sentences for each aspect. We used product reviews from Yandex.Market as target data. Our task was performed in five main phases: 1) expert identification of thematic aspect categories; 2) classifying sentences into the predefined aspect categories; 3) sentence classification into two classes—positive and negative—with calculating the number of both type sentences within each aspect category; 4) sentence ranking; 5) visualization of the results obtained in the previous phases. The quality of the algorithm for creating a resume from a large collection of reviews has been tested on five models of products from the following categories: coffee machines, robot vacuum cleaners, e-books, TV-sets, and washing machines.