  [Purpose] To better understand the online reviews and help potential
consumers, businessmen, and product manufacturers effectively obtain users'
evaluation on product aspects, this paper explores the distribution
regularities of user attention and sentiment toward 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 employed
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 user attention and sentiment toward product aspects in this
article. [Findings] The empirical results show that a power-law distribution
can fit user attention to product aspects, and the reviews posted in short time
intervals contain more product aspects. Besides, the results show that the
values of user sentiment of product aspects are significantly higher/lower in
short time intervals which contribute to judging the advantages and weaknesses
of a product. [Research limitations] The paper can't 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 user
attention and sentiment toward product aspects, which is of great significance
in assisting decision-making, optimizing review presentation, and improving the
shopping experience.
