Potential Customers Recognition Method Research Based On Domain Sentiment Analysis

Potential customers refer to a group that can bring profits to enterprises in the future development stage.Accurate identification of potential customers can help enterprises in many aspects,such as precise marketing,expanding market share of products,improving core competitiveness,and so on.It is an important research content of enterprises.Usergenerated content in social media environment contains abundant emotional information,including emotional attitudes towards product needs,brand perception,purchase intention,etc.The research shows there is a correlation between user's emotion and purchase intention,thus emotional information can help enterprises find potential customers.However,the main tool of affective analysis is affective dictionary,which cannot adapt to domain dependence,cannot cover network new words in social media in time and accurately identify the emotional tendency of unknown candidates.Therefore,the construction method of affective dictionary in research field is necessary.At the same time,in the process of identifying potential customers,due to the irregular content text generated by users,the changing emotions with the theme and the low proportion of target potential customers,these problems bring difficulties to the identification of potential customers.This paper focuses on the automotive field under social media,constructs an emotional dictionary for text emotional analysis,and applies the emotional dictionary to potential customer identification tasks under unbalanced data sets,designs a potential customer identification method that combines emotional topics in the field to find potential customers with car purchasing intention.Firstly,this paper reviews the current research status of text affective analysis and potential customer recognition,and introduces the construction process of affective dictionary and feature selection,feature selection and commonly used classification algorithms in text analysis.Secondly,based on the most commonly used tool affective Dictionary of affective analysis,this paper studies the rules of affective polarity discrimination of affective words.Aiming at the shortcomings of using single recognition algorithm in the process of constructing emotional dictionary,an improved integration rule is designed,and an automatic construction method of domain emotional dictionary in social media environment is proposed.Then,this paper analyses the emotional subject information in user-generated content,and adds domain emotional dictionary to the joint emotional subject model as a posterior information to extract emotional subject features.On this basis,the feature Engineering Research of potential customer recognition is carried out and the feature set is constructed.In addition,in view of the class imbalance in the actual data,a sample resampling method and a multi-integration framework for imbalanced data are designed to work together on the identification task of potential customers under data skew.Finally,the real social media text corpus is used for the experimental study to validate the proposal methods.The comparative experiments show that the proposed method of constructing domain emotional dictionary and the method of identifying potential customers of joint domain emotional topics show good performance in different control experiments.In this paper,the construction of domain emotional dictionary and unbalanced classification are deeply studied in theory,and solutions are provided for enterprises to find potential customers in practice,which has certain theoretical significance and practical value.