The invention discloses an
electric power customer service work order sentiment quantitative
analysis method based on Word2Vec, and relates to an
electric power customer service work order
analysis method. A traditional
sentiment analysis method cannot effectively discriminate the sentiment intensity. The method of the invention comprises the steps of combining the power customer service work order text features; classifying and sorting the historical
electric power customer service work orders and the unsatisfied work orders, cleaning data,
combing based on the Baidu word
bank to form an initialized multivariate emotion word
bank; carrying out the work order text word segmentation by adopting a reverse maximum matching
algorithm; based on the Word2Vec neural network, constructing the positive words, negative words, degree adverbs and a word vector of a
word order fused with customer appeal
semantics; performing the
machine learning training through the historical customer service workorder to generate a learning model fusing appeal emotion, expanding a part-of-
speech corpus based on the part-of-speech affinity-
consanguinity relationship in the model, performing emotion quantization calculation by adopting a similarity word sequence matrix quantization
algorithm, and completing customer service work order emotion quantization analysis, thereby effectively distinguishing emotion intensity differences, and determining an emergency degree.