Research On Time Series Correlation Rules Of Online User Reviews Behavior

The rise of Web2.0 was dominated by the enterprise and business information network to generate and created by consumers,the production and dissemination of the network information users called user generated content(UGC),online user reviews is an important way of user generated content,open online publishing user consumer experience in text content in the form of shopping the process of perception,purchase and comment on the user behavior in the electronic business platform and business platform also records the time trajectory of consumers,consumer behavior characteristics of different comments usually have different habits of time behavior,analysis of user characteristics by time records,for consumers,businesses,platforms and vendors to provide different time the purchase of goods,review of information law,but also an important channel for user behavior analysis and commodity recommendation.This paper summarizes,in the domestic and foreign online user reviews research results on the analysis of the integrated use of literature analysis,empirical analysis,knowledge discovery research and analysis,social network analysis,based on the technology acceptance model(TAM)framework,the study found that from the external factors,usefulness and emotional factors in 3 dimensions the characteristics of online user reviews of behavior knowledge.This research mainly includes six aspects,the third chapter research on time mechanism of online user reviews,analyzes the inner relationship between the online user reviews and comments on the behavior of the time,is the theoretical basis of the thesis;the fourth chapter is based on the theory of TAM,the online user comments on the impact on time factors research,analysis the trend of external factors,usefulness and emotional factors impact on the overall review of time;fifth to the seven chapter,the usefulness of external factors and emotional factors based on the detailed distribution in each index time interval sequence factors;the eighth chapter conclusion based on the above analysis,put forward some management strategies are applied to practice.Specifically:The third chapter focuses on the study of the time characteristic mechanism of online user reviews.First,we analyze the behavior mechanism of online user reviews,then analyze the driving relationship between online user reviews behavior and comment time,analyze the online user reviews behavior time characteristics,and finally build a theoretical model of online user reviews behavior time characteristics rule.The fourth chapter focuses on the influence factors of time online users,and application of improved technology acceptance model to extract the indexes,including online reviews,external data reviews’ helpfulness and comment on emotion,by establishing regression analysis model,time series characteristics of consumer behavior are verified comments.The results show that the positive effect on the timeliness of user level reviews,timeliness of commentary on the star rating has a negative impact,the negative effect of the quality of comments online user reviews,has a positive impact on the timeliness of the emotional intensity of comments,points like the number and depth of semantic comment period effect is not significant.The fifth chapter is a study of the temporal sequence correlation characteristics of user reviews based on external data.This chapter will review the external user data as technology acceptance model index,dynamics characteristic index correlation analysis of time series based on user reviews,puts forward the innovative method of interval division comments for time series,commented on the characteristics of metadata content correlation analysis and knowledge discovery of time sequence.The sixth chapter reviews the usefulness of the analysis of user comments behavior of time series association characteristics based on the technology acceptance model is useful and easy to use conversion index for application study on factors of the quality of online user reviews in this paper,through text mining method,numerical method and Naive Bayesian classification method of fuzzy comprehensive evaluation,sorting and classification of quality review comment on the different time series of interval,mass distribution were classified,and the analysis of product quality review feature word,emotional words and emotional words and distribution characteristics of the degree of Association for the study of online reviews,quality time series correlation characteristics of knowledge discovery.The seventh chapter sentiment analysis of online user reviews behavior characteristics based on time series Association,user reviews affect acceptance model using attitude index technology,through the text semantic emotion mining method in different time interval sequence polarity and intensity of emotions and emotional semantic correlation were analyzed by frequency statistics for different time intervals summary of emotional words and different emotional emotion clustering time series polar distribution,discovered the law of time series association characteristics of emotional comments.The eighth chapter based on the characteristics of the time found that the proposed countermeasures of management,the research paradigm of "time-Information-based",from the angle of time on each time period,the user behavior characteristics of different time management application object;from the perspective of information management,time distribution model of the 3 aspects of external factors,useful sexual and emotional factors;information from the human point of view,to consumer,business platform,sales companies and manufacturers of the 4 aspects of the corresponding management implications,and finally put forward specific management strategies.The study on the theoretical level,improve the online user reviews on system structure,make up the research field at the present stage of grain size of the coarse,ignore the dimension of time and research system is not perfect,provides a theoretical framework for the system of time characteristic analysis of online user reviews behavior;in practice,can provide guidelines for the purchase decision for consumers,to guide businesses to improve service and management,to help the upstream manufacturers to optimize production,improve the operation and management mode of e-commerce platform.