Research On User Reviews Of TWS Headset Based On Text Mining

With the advent of the era of AI and 5G,the application scenarios of smart wearable devices in people’s lives are becoming more and more extensive.Today,The idea of the Internet of everything is popular.As one of the wearable devices that have grown in global sales in recent years,the TWS headset It has got rid of the shackles of the cable and has the advantages of easy portability,intelligent noise reduction,etc.,and has been favored by the majority of consumers.At the same time,Online shopping is gradually becoming a way of life for people.Due to the complex properties of electronic products,the standardized management of e-commerce platforms and the seven-day no-reason return policy,consumers are more willing to buy electronic products online than offline stores.product.The content of user reviews has great research value as a reference for consumers before purchasing and as a basis for manufacturers to optimize follow-up products,and e-commerce user reviews are often tens of thousands.Therefore,researching these massive texts to dig out important information has strong research value.realistic meaning.This thesis introduces the method of text mining into the research on user reviews of TWS headsets.First,we use crawler technology to obtain the user review data of TWS headsets from Apple,Huawei and Xiaomi in Jingdong Mall,and then use data cleaning,Chinese word segmentation and stop word processing.The high-frequency keywords and their relationship with each other are displayed in the form of word cloud graph and semantic network graph.Then,the support vector machine model and the long short-term memory model are used to construct sentiment classifiers to analyze the sentiment tendency of user comments respectively.Finally,the LDA topic is used.The model mines the potential themes of positive and negative reviews,so as to clearly understand the user’s satisfaction and pain points for the product.On the one hand,potential consumers can make multi-dimensional comparisons based on their own needs and product characteristics to help them make scientific purchasing decisions;on the other hand,manufacturers Targeted optimization can also be carried out on the later products according to the user’s pain points to further improve the user experience.After analysis,the conclusions show that,first,when classifying the sentiment tendency of TWS headset user comments,the classification effect of the long short-term memory model is better than that of the support vector machine model.Second,the analysis of the LDA model shows that Apple TWS earphones mainly have positive features such as clear sound quality,strong noise reduction ability,and easy to use,and negative features such as general after-sales service,insufficient battery life,and low cost performance;the positive features of Huawei TWS earphones include appearance,Application scenarios and after-sales service;negative features include sound quality,battery life,and price.Xiaomi TWS earphones have positive features such as reasonable price,high cost performance,and strong battery life,as well as negative features such as general sound quality,connection delay,and general workmanship.In view of the deficiencies of various brands of earphones and the problems existing in the e-commerce platform,the following suggestions are put forward.Apple earphone manufacturers need to further improve the battery life and battery performance of the earphones.Huawei headset manufacturers need to improve sound quality and improve noise reduction capabilities.In addition to improving sound quality and noise reduction,Xiaomi headset manufacturers also need to work hard on the workmanship of the headset.E-commerce platforms need to reduce the risk of damage to express packages and improve the quality of after-sales services in order to maintain a good reputation for the platform.