Data Analysis Of Commodity Reviews Based On Deep Learning

With the rapid development of the Internet,online shopping has gradually become one of the most common means of shopping in people's daily life.Online shopping is convenient and fast,and there are many kinds of goods.However,consumers cannot intuitively see the real attributes of goods in advance.In order to know more about the real situation of commodities,consumers can easily obtain guidance preferences by viewing online commodity reviews information before purchasing commodities,which can be used as a helpful reference for purchasing decisions.However,while online review functions bring convenience to consumers,the quality and accuracy of individual review data will also lead to misjudgments or even misleading to potential consumers in the future,such as non-objective reviews caused by subjective differences of consumers,imperfect commodity information,excessive packaging of businesses,etc.Therefore,it is of great significance for consumers to obtain comprehensive reviews information of commodities to effectively identify the quality of commodities.However,there is an irreconcilable contradiction between mass commodity reviews information and consumers' ability to browse and analyze manually.When the reviews data reaches a certain order of magnitude,it is very difficult for consumers to obtain and analyze the data manually.Therefore,it is of great practical and commercial value to develop a system which can automatically collect data,analyze data and emotions,and provide users with practical and high reviews value data.Data analysis of commodity reviews based on deep learning in this paper,has the functions of data collection,data cleaning,eigenvalue extraction,word vector modeling,and deep learning modeling to realize data sentiment analysis.Through this system,users only need to select the designated shopping website,choose the ordering method of goods,and input the keywords of inquiring goods.The system will automatically collect and analyze the commodity information,and return the data with high reference value.Through simple sliding on the page,Users can easily browse comprehensive evaluation information of a large number of commodities.At the same time,by adjusting the corpus,word vectors and parameters of the model,the system carries out a lot of deep learning model training,so as to summarize and refine a corpus in the field of e-commerce shopping,which greatly improves the accuracy and validity of data analysis results.