A Study On User Satisfaction Of Audiobook APP Based On Online Reviews

With the rapid development of mobile internet technology,Audio books APP is favored by the majority of user groups.However,there are some shortcomings and defects in the process of using various audio book APP.Developers still need to continuously improve the system quality,content quality and service quality in order to meet the diversified requirements of the user group,so as to improve the user satisfaction of audio books APP and can develop rapidly in the fierce market competition.Based on the current lack of user satisfaction research on audio book APP,this paper takes online comment as the analysis corpus,first uses the Text Rank algorithm and the pre-training language model to construct the feature words with sound reading APP,and then uses the Canopy K-means method to obtain more scientific evaluation dimension results through coarse clustering and fine clustering.Finally,the feature words under each dimension are scored by emotion word dictionary,degree adverb dictionary and negative word dictionary,and the emotional score of feature words is obtained,and then the user satisfaction and overall satisfaction of each evaluation dimension are calculated.Based on the above calculation results,this paper draws a four-point graph and a relative performance graph to analyze.On the one hand APP it can enrich the theoretical research in the field of audio book APP by comparing and analyzing the different evaluation dimensions within the same audio book APP and the same evaluation dimension.On the other hand,for audio book APP in the future development and operation of constructive advice.