燃气行业热线数据的情感分析

Sentiment Analysis of Hotline Data in Gas Industry

客服热线的情感分析对企业核心业务的发展具有决策作用,能提升用户的忠诚度。传统的热线情感分析方法采用的是人工记录或随机采样方式,这样不仅耗费人力,而且无法保障准确率,关键在于其不能客观反映客户的情感,从而最终影响企业的业务质量。结合项目背景,针对燃气公司现有的离线音频文件,提出了声学特征和领域情感词典混合算法,并将其应用于客服热线数据的情感分析以及客户情感(负向、非负向)的识别中;最后,通过召回率、准确率和精确率衡量了算法性能。实验选取1500个音频文件作为数据集,其中负向和非负向数据集均为750个。实验结果表明,该算法在项目实践中具有较好的识别效果,尤其是与领域情感词典的结合。

Sentiment analysis of customer service hotline plays a decisive role in the development of enterprise core businesses,and can enhance customers’loyalty.Traditional hotline emotional analysis methods use the ways of manual recording or random sampling,which not only consume manpower but also can’t guarantee accuracy,and the main problem is it cannot reflect customer’s emotion objectively,and ultimately affects the quality of service enterprises.Accor-ding to the background of the project and the existing offline audio files of Gas Company,hybrid algorithm of acoustic features and domain sentiment lexicon was proposed,which is used in the data analysis of customer service hotline and identifying customer sentiment(negative,non-negative).The experimental results show that the algorithm has an efficient recognition effect on the project practice,especially the combination of field of the sentiment lexicon.