Exploring the Distribution Regularities of User Attention and Sentiment toward Product Aspects in Online Reviews

TL;DR

This paper investigates how user attention and sentiment towards product aspects in online reviews follow certain distribution patterns over time, revealing insights into review dynamics and sentiment variations.

Contribution

It uncovers distribution regularities of user attention and sentiment in online reviews using large-scale smartphone review data, highlighting temporal patterns and their implications.

Findings

User attention follows a power-law distribution.

Reviews in short intervals contain more product aspects.

Sentiment values vary significantly in short time intervals.

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 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.