Based On Text Mining For The Online Medical Platform Analysis Of Subject And Emotion

With the continuous development of society,more and more people’s understanding of medical care has changed from going to the hospital when they are sick to paying special attention to their own health management on a daily basis.In recent years,various online medical platform websites have emerged on the Internet.With the help of in-depth mining and analysis of the online medical platform,we can understand the subject content and emotional changes of the hot topics that users are concerned about,and can also provide users with a more convenient way to find and obtain information.On the Internet,most of the past research on online medical care was counted manually.However,due to the rapid development of modern technology,the traditional text analysis technology becomes more and more unsuitable when dealing with a large amount of text.Therefore,machine learning is slowly being used in the research of network medical platforms.As a common way of information exchange,user evaluation often has the subjective color of the user in the evaluation text,and its most notable features are the theme and emotion.For this reason,this thesis uses the methods of topic analysis and sentiment analysis to deeply analyze the changes of hot topics and sentiments.This thesis makes an in-depth analysis of the user evaluation results of the online medical platform,Haodaifu Online Skin Beauty Department,and uses thematic analysis and sentiment analysis to analyze the platform’s user evaluations thematically,and analyzes emotional preferences.Inquiry has enriched relevant research to some extent.In addition,in practical applications,this thesis adopts the medical platform data of Good Doctor Online.For platform operators,it can provide better management suggestions and promote the development of the platform,so as to provide better services for patients;for medical professionals,can provide a richer perspective,allowing medical staff and patients to communicate,so as to provide diagnostic services according to the needs of patients.At first,it introduces and analyzes related theories such as online medical platform,medical beauty,sentiment analysis and topic analysis,analyzes the problems existing in topic analysis and sentiment analysis of online medical platform combined with theories,and makes specific theories on sentiment analysis,topic analysis and other related theories.’s discussion.Then,considering the distribution characteristics of medical vocabulary in the online medical platform and the characteristics of short and nonstandardized texts,the LDA topic model was used to fit the distribution of vocabulary based on the topic of the sentence,and the correlation weighting was used to correct the word frequency expansion sentence..Next,the sentiment polarity is discriminated on the user evaluation texts under each theme.Finally,the research is carried out based on this.The results show that:(1)The themes of medical beauty user evaluation on the online medical platform are divided into 5 categories: feedback on service attitude of follow-up consultation,evaluation of outpatient service,feedback on operation effect,condition change,and feedback on laser treatment.(2)Most of the evaluations of medical beauty users on the online medical platform are positive emotions,followed by peaceful emotions,and negative emotions account for the lowest proportion.