Work order classification method and device

Abstract

The invention provides a work order classification method and a work order classification device. The method comprises the steps of performing word segmentation on a work order to obtain a word set; acquiring a ratio of positive part of speech words and negative part of speech words in the word set according to a part of speech dictionary; if the ratio of the positive part of speech words and thenegative part of speech words satisfies a preset threshold value, determining that the work order is a work order category corresponding to the preset threshold value; and if the ratio does not satisfy the preset threshold value, judging the word set by use of a trained naive bayesian classifier to acquire the work order category to which the work order belongs. According to the method, preliminary classification is performed through the ratio of the positive part of speech words and the negative part of speech words in the word set so as to reduce the work order quantity needing to be classified by the naive bayesian classifier and improve the classification efficiency; and the method is suitable for classification of the mass work orders in the telecom industry and has relatively high accuracy.

Description

technical field [0001] The present invention relates to the field of communication technologies, in particular to a work order classification method and device. Background technique [0002] Customer service representatives of mobile operators need to process work orders for public opinion evaluations from all aspects of the Internet, such as status and comments from social networks such as Weibo and Moments. Customer service representatives have to face a lot of information every day, and work orders may have Different categories such as complaints, suggestions, consultations, praises, etc., in order to facilitate customer service representatives to process a certain type of work orders, such as priority processing of complaint work orders, so as to improve the efficiency of customer service representatives in handling work orders, so These work orders need to be categorized. [0003] In the prior art, the naive Bayesian algorithm is usually used to classify texts, which i...

AI Technical Summary

Problems solved by technology

In the face of the classification of massive work orders, a large amount of computing resources are required. Under the premise of limited computing resources, the performance of classification efficiency is reduced, resulting in a backlog of work orders, and work orders cannot be processed in time.

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