Research on Object-level Knowledge-enhanced Sentiment Analysis Model in the Automotive Field
In response to the need for sentiment classification analysis across various indices in automobile reviews,this study intro-duces two tasks:identifying the objects of car evaluations and extracting emotional components,and conducting sentiment classification analysis augmented by emotional knowledge.Leveraging the Point Mutual Information(PMI)method,the study elucidates the relation-ship between object words and emotional words,further utilizing textual emotional component analysis to establish an emotion-knowl-edge-enhanced Object-Level Sentiment Analysis Model(OLSCA).Initially,the model employs PMI to discern the relationship be-tween key evaluation indicators and the polarity of emotional words.Subsequently,it generates predictive targets for words,word polar-ity,and object-level emotional relations via automated emotional word masking and emotional object predictive analysis,yielding senti-ment classification outcomes for tagged objects.Experimental results demonstrate that OLSCA,when executing sentiment classification analysis on succinct reviews in the car evaluation domain,offers substantial practical utility compared to traditional text semantic senti-ment analysis,facilitating the comprehensive development of a car evaluation framework grounded in user evaluation intent.
PMIobject-level sentiment analysisemotion knowledge enhancementuser evaluation intentvehicle evaluation system
NETL
NSTL
万方数据
