User Demand Identification and Development Trend Prediction Based on RF-BERT and UGC
[Purpose/significance]Mining user potential needs through user-generated content(UGC)in social networking platforms helps companies gain insight into market trends,optimize product development,and improve promotional strategies.[Method/process]Firstly,using TF-IDF technique and K-means algorithm to extract product attribute features from online comments,secondly,calcu-lating the sentiment and attention values of each user's comments on the product attributes,and then filtering and categorizing the product attribute features in the user comments through the RF-BERT model.Finally,using the Bi-LSTM model to predict the fluctua-tion of product attribute sentiment and attention after classification,and thus obtain the development trend of user demand.[Result/conclusion]Taking the online comments of"qichezhijia"as an example,the experimental results reveal the influence degree of differ-ent product attribute features when user comments are defined as selected comments,showing the fluctuation of sentiment and atten-tion degree of product attributes with higher impact values.[Innovation/limitation]The paper proposes a technical solution for user de-mand identification and development trend prediction,which provides a reference for companies to formulate promotion strategies and develop innovative products,but the scale of the research data is relatively small,and future research will expand the sample size and focus on the generality of the research results.
text miningdemand trendsuser generated contentemotional analysisproduct attributes
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