Research On Cross-border E-commerce Food Selection Based On Text Analysis

In recent years,the continuous introduction of opening-up policies and global integration strategic deployment,and the rapid improvement of logistics supply chain efficiency have provided impetus for the rapid development of cross-border e-commerce.Facing consumers with different consumption habits in different regions,it is particularly important for cross-border e-commerce companies to choose marketable products in the first step that want to survive and develop in a highly competitive and constantly updated market environment.Merchants need to combine reality and gain insights into current social consumption conditions and online shopping consumers' emotional changes to choose and optimize products in order to improve consumer satisfaction and make profits.At present,scholars use the online product reviews of e-commerce websites as the starting point to research the shopping needs of foreign consumers.Most of them are limited to the product selection of domestic e-commerce websites and lack research on product selection in the food field.Based on this,the paper conducted opinion mining on the massive online reviews of the food section of Amazon‘s cross-border e-commerce website,constructed a food evaluation system,implemented different text analysis methods,to solve the problem of how cross-border e-commerce in the food industry chooses marketable products.The research content can be listes as following four items:First: A method for measuring the popularity of food based on the TF-IDF algorithm is proposed.First,extract keywords through TF-IDF to get the food themes discussed in each review,and perform word frequency statistics and classification statistics on the food theme keywords of each review,and finally get the most popular best-selling food kinds.Second: A method of structured food review data features based on deep learning is proposed.First,use the LSTM model in deep learning to classify the data and extract the emotional features of food reviews.Then taking into account relevant literature research and the research background of the cross-border e-commerce industry,an index evaluation system containing fifteen dimensional characteristics thesaurus for the evaluation of cross-border e-commerce export food was constructed.And then based on the Word2 vec model in deep learning,the key dimensions of food characteristics were expanded.Finally,extracts the useful features of each review text based on the emotional classification results and the constructed product feature thesaurus,and converts the review data into structured feature data.Third: Propose a research method of consumer online shopping preference based on association rule algorithm.The constructed structured data composed of product features and emotional features was tested through association rule algorithms for the support of each feature and the association rules between features,and the food features that consumers paid attention to and praised under different conditions were discussed.A comparative analysis of the commonalities and characteristics of the important product features of the company,and suggestions for feature optimization are put forward.Fourth: Based on the overall importance,difference and attention of the feature,make suggestions to the merchant from the perspective of operation and management.From the perspective of consumers,the thesis understands the needs of consumers' feedback in a timely manner through comments,and then specifically optimizes food characteristics and business operation plans,expands product appeal,and provides crossborder e-commerce merchants with practical product selection ideas.