Research On The Impact Of Online Comments On The Price Of Household Appliances

Online shopping has become an increasingly common shopping mode in modern life.Statistics show that more than 30% of consumers will increase their knowledge of products by reading online reviews when buying unfamiliar products online.Previous studies have shown that online reviews can influence consumers' purchasing needs and intentions,and then decide whether to buy or not.The sales volume of products will change,when consumers' demand for commodities changes.Therefore,the merchants on the network platform can no longer fully adopt the traditional way of commodity pricing,but need to pay more attention to the impact of online reviews,and use dynamic pricing strategy to obtain greater benefits.In addition,online reviews usually reflect consumers' satisfaction with goods or services,and are an important channel for merchants to get feedback from consumers.Through the analysis and mining of online reviews,we can find out the shortcomings of our products and services and make corresponding improvements.In order to analyze the content of online reviews,it is necessary to conduct emotional analysis and opinion mining for online reviews.At present,the research on English online review has been relatively mature at home and abroad,but due to the particularity and complexity of Chinese,the research on Chinese online review is still developing.Firstly,by sorting out the existing literature,this paper sorted out three commonly used analysis methods: Baidu-AI platform-based functional interface invocation technology,machine learning based on part-of-speech path extraction algorithm,and manual tagging.Then,by using content analysis method and computer-aided technology such as natural language processing,emotional analysis and opinion mining are carried out for online commentary of intelligent TV sold in Jingdong.When using machine learning method to mine opinions of online comments,we find that the original algorithm based on "display" part-of-speech path extraction is not ideal,so this paper enriches the algorithm of part-of-speech path extraction,and adds "implicit" part-of-speech path to the extraction template.Finally,based on the manual labeling method,the advantages and disadvantages of the other two methods are compared.After obtaining the relevant data describing the comment content,such as comment emotional tendency and opinion emotional tendency,this paper puts forward the hypothesis of the relationship between online comment sentiment tendency and price,and verifies the hypothesis.This paper takes online reviews and prices of two smart TVS with good sales performance on Jingdong as research objects.On the basis of indexing the data of comment emotional tendency and opinion emotional tendency,the correlation between all the indexes and price is analyzed by SPSS software.As an important channel for reviewers to collect information,online reviews affect consumers' purchase decisions on the one hand,and on the other hand,they are the basis for businesses to make decisions on the problems reflected in reviews.This paper analyses the online review data,extracts useful information,explores its relationship with prices,assists businesses to formulate dynamic pricing strategies to obtain greater profits,and helps consumers to choose the right time to buy.At the same time,this research method can be applied to the study of other commodities,and the critical sentiment analysis and opinion mining technology can also be extended to other Chinese text processing.