Exploring the distribution regularities of user attention and sentiment toward product aspects in online reviews

Abstract

Purpose To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews. Design/methodology/approach Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper. Findings The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product. Research limitations/implications This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms. Originality/value This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

Which structure of academic articles do referees pay more attention to?: perspective of peer review and full-text of academic articles

“…The method of clustering we used in this paper was proposed in the work of Qin et al (2021). In this paper, the Doc2Vec model (Le and Mikolov, 2014) was used to generate vectors for abstracts of the experiment data.…”

“…They divided the articles into four parts based on the section title, namely, Introduction, Method, Results and Conclusions (IMRC). Zhang et al (2021) explored the distribution of adverbs and adjectives used by reviewers in different structures of academic papers based on 3,329 review reports from the British Medical Journal. They divided the structures into seven parts by manual: Overall, Abstract, Introduction, Methods, Results, Discussion and Other.…”