在线医药电商评论情感分析——基于XGBoost集成加权词向量和大语言模型的情感识别模型

Reviews Sentiment Analysis of Online Pharmaceutical E-commerce:Sentiment Recognition Model Based on XGBoost IntegratedWeighted Word Vector and Large Language Model

消费者评论是考察消费者情感的重要数据源,对商品评论进行数据挖掘是帮助在线医药电商改善经营的重要途径。立足于在线医药电商的用户评论,基于SO-PMI(情感倾向点互信息)算法构建该领域情感词典,对评论词向量进行情感加权。利用XGBoost(极限梯度提升树)集成词向量和LLM(大语言模型)构建情感识别模型,最后得出评论情感指数,从多个维度展开,分析消费者评论中的情感趋势。实证分析表明,构建的情感识别模型的AUC(曲线下的面积)等验证指标较LLM模型相比有进一步提升,具有一定的应用价值。

Consumer reviews are an important data source to investigate consumer sentiment,and data mining of product reviews is an important way to help online pharmaceutical E-commerce improve business.Based on the user review text of online pharmaceutical e-commerce,a sentiment dictionary for online pharmaceutical e-commerce based on the SO-PMI(semantic orientation pointwise mutual information)algorithm is constructed,then integrate weighted word vectors and the LLM(large language model),train classifiers are used to construct a sentiment recognition model using the XGBoost(extreme gradient boosting)algorithm.Finally a sentiment index to analyze the sentiment tendency of the online pharmaceutical E-commerce consumer reviews is established from multiple dimensions.The empirical analysis shows that the validation indexes of the constructed emotion recognition model such as AUC(area under curve)are further improved than that of the LLM model,which has certain application value.