An electric power customer service work order sentiment quantitative analysis method based on Word2Vec

Abstract

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.

Description

technical field [0001] The invention relates to a method for analyzing work orders of electric power customer service, in particular to a method for quantitatively analyzing emotion of work orders of electric power customer service based on Word2Vec. Background technique [0002] With the development of social economy and the continuous deepening of power system reform, power supply companies can only gain market-oriented competitive advantages by adhering to customer-centricity and improving customer satisfaction. As an important channel window for customer communication and communication, 95598 realizes quantitative emotional analysis of customer appeals through deep mining of customer characteristics and emotional information hidden in customer service work orders, which is conducive to quickly understanding the focus of customer attention, and is conducive to according to customer Identifying potential complaining customers with emotional tendency is conducive to support...

AI Technical Summary

Problems solved by technology

[0005] Traditional sentiment analysis methods are based on bag-of-word features and word frequency statistics. At the same time, most studies focus on the classification of sentiment orientation. This method has the following three defects: (1) The context order and semantic understanding of missing words ; (2) Ignoring the semantic difference between words; (3) Focusing on the classification of emotional orientation can not reflect the difference in emotional strength

In short, it cannot effectively identify the intensity of emotion

Images