Research on the Demand of Online Pharmaceutical Consumers under the COVID-19 Based on Text
The sudden outbreak of COVID-19 has stimulated consumers'online purchasing behavior,resulting in an explosive growth in the scale of pharmaceutical e-commerce transactions and an increasingly rich content of online comments on pharmaceutical e-commerce.The online reviews of pharmaceutical e-commerce contain a variety of information,including not only the overall star rating of consumers'purchasing experience,but also detailed text comments,which hide consumers'subjective feelings and consumption needs for product purchases.In order to promote the healthy development of pharmaceutical e-commerce and better meet the medication demand of consumers during the epidemic,it is urgent to carry out research on the demand of pharmaceutical online consumers under the COVID-19.From a theoretical perspective,this study focuses on online reviews of pharmaceutical e-commerce,and expands the application fields of text mining methods.From a practical perspective,this article studies the information contained in online reviews of pharmaceutical e-commerce,which can help pharmaceutical e-commerce better catch the sour spot consumer demand,timely identify problems in operating pharmaceutical e-commerce platforms,provide practical suggestions for platform operation and development,and improve consumer purchasing experience and service quality.This study uses the Python crawler tool to collect online comment data from a certain pharmaceutical e-commerce platform in 2019,2020,and 2021,and captures a total of 176602 data from 17 categories of products.By processing data cleaning,word segmentation,and word frequency statistics,high-frequency words in online reviews of pharmaceutical e-commerce are extracted and displayed through word cloud maps.Then,the LDA theme model is used to further analyze the semantic relationships behind high-frequency words,in order to better understand the connections between high-frequency words.By summarizing each theme,the concerns and needs of consumers are clarified.Next,we construct a sentiment analysis model to classify emotions in online comments.The first step is to calculate the sentiment value of the text based on the Boson NLP sentiment dictionary.The second step is to train text at the word and word levels based on the BERT model beforehand.The third step is to connect the sentence vectors obtained in the first two steps and input the new sentence vectors into the SVM classifier for classification.The fourth step is to test the emotional classification performance of this model.The fifth step is to perform sentiment classification on all online comment data,including sentiment classification for individual text comments and individual topics.This study focuses on analyzing online reviews of negative emotions,as negative reviews often contain more suggestions related to products or services,which can help pharmaceutical e-commerce understand consumer sour spot and improve service levels.The main conclusions of this study are as follows:Firstly,consumers always pay attention to the effective-ness of medication use,logistics services,product prices,platform reliability and safety when purchasing phar-maceutical products online.Secondly,by comparing and analyzing the high-frequency words in online comments throughout this three-year epidemic,it can be found that before the COVID-19 broke out in 2019,consumers paid great attention to service attitude,commodity brand and purchase convenience.After the COVID-19 just broke out in 2020,consumers paid more attention to cold and vitamin medications,which may be because these medications help to prevent,control and cure COVID-19.In addition,the outbreak of the epidemic will affect consumers'medication purchase decisions,and gradually cultivate consumers'habit of purchasing medications online.In the late stage of the epidemic in 2021,consumers were more concerned about the cost-effectiveness of goods,the speed of purchase,and the quality of medication.Thirdly,consumers generally show a positive emotional attitude towards purchasing medications on pharmaceutical e-commerce platforms,with over 85%of positive comments.Fourthly,the negative online comments are mostly about medication prices,efficacy,quality,purchase of prescription,platform reliability,logistics packaging,and purchasing experience during the epidemic.Although this study has achieved certain research results,there are still certain limitations.For example,taking online reviews of pharmaceutical e-commerce as the research object,the amount of online review data obtained from this pharmaceutical e-commerce is sufficient but the subject is single.In addition,the emotional analysis model lacks comparative experiments to further verify the superiority of the model,and these issues can be continuously explored and improved in subsequent research.
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