[Purpose/significance] According to the text length,the online experiential product review is divided into long text online review and short text online review. Exploring the temporal and content characteristic of these two types of online review provides intelligence basis to e-commerce platform about consumers' online review behavior and product demand preference.[Method/process] Python crawler language is employed to collect information of online review in movie review website,and then the paper constructs an online comment interval sequence. Human behavioral dynamics theory is used to find out time characteristic law in different types of online review,and on the other hand,text mining method is used to discover content characteristics in different types of online review. The characteristics are compared and analyzed in the paper.[Result/conclusion] Taking the movie review websites' online reviews as the data source, from the time perspective,this paper concludes that time interval sequence obeys to the power-law distribution between different types of online review behavior,and from the text mining perspective,it finds that the content characteristics performance similarities as well as significant differences.
Wang Jun
,
Li Zijian
,
Liu Xiaoman
. Study on Time Series of Online Experiential Product Review Based on Text Length: Taking Movie Reviews as an Example[J]. Library and Information Service, 2019
, 63(16)
: 103
-111
.
DOI: 10.13266/j.issn.0252-3116.2019.16.011
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