The Research Of Big Data Analysis Methods In Social Commerce

With the coming of the era of big data, analysis technology is widely used facing massive, heterogeneous and unstructured data. As a new mode of e-commerce, Social commerce is the integration of the social interaction function of social media and the business function of e-commerce, and it has become an important source of big data. Furthermore it is the large amounts of unstructured data, especially the text data that is of great commercial value. These complex text data often implicitly contains the users’ preferences, behavior habits, consumption tendency, etc. The analysis of these text data is helpful to promote the development of social shopping and social marketing in social commerce. Under this circumstance, the big data analysis of social commerce is both urgent and necessary. These text data in social commerce is analyzed with big data methods.First of all, the complexity of the commercial data of social commerce is analyzed. The social commerce data contains the typical characteristics of big data. Then the unstructured data is analyzed in aspect of complexity in social commerce.what is more important is the discussion of the complexity of data management and Chinese text data mining in social commerce, and the framework of Chinese text data analysis is given to the further understanding of its mining.Secondly, a method is proposed about using Apriori algorithm to extract the product features, and the method is applied to the sentiment analysis of product reviews in social shopping. In the field of social commerce, many users have commented on many products, leaving complicated and unstructured text data. The complex unstructured feature makes trouble for the users’retrieving of the reviews. Based on the above, a sentiment mining of product reviews is made in social commerce, that is, proposing a method of mining frequent features of products based on Apriori algorithm and discussing what the pruning operation of frequent features is to improve the effectiveness. Finally, semantic dictionary HowNet is used to determine the emotional tendency of opinion words.Thirdly, an EM-LDA integrated model is proposed to identify hot topics of E-commerce microblog. E-commerce microblog is a kind of unstructured text information carrier, but it has some characteristics differing from unstructured text data. Through the analysis of the E-commerce microblog, an EM-LDA integrated model of E-commerce microblog is proposed. Firstly this integrated model makes a classification of E-commerce microblog data according to whether containing the hashtag. The ET-LDA model is used to analyze microblogs with hashtag, which is called explicit topic microblog. And the other microblogs without hashtag is analyzed by IT-LDA model which is called implicit topic microblog, and the IT-LDA model is an improved LDA model according to the classification of implicit topic microblog.