Customer service early warning analysis system based on deep learning
The invention discloses a customer service early warning analysis system based on deep learning, and particularly relates to the field of customer services. The system comprises a first session collection module which is connected with a session classification module and is used for obtaining a curr...
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| creator | MA JIQIAO LUO RUIXIONG |
| description | The invention discloses a customer service early warning analysis system based on deep learning, and particularly relates to the field of customer services. The system comprises a first session collection module which is connected with a session classification module and is used for obtaining a current session text in real time and processing the current session text to obtain a current session feature; the second session collection module, the session classification module and the session text database are connected, and the second session collection module is used for obtaining the session text of the previous moment from the session text database and processing the session text of the previous moment to obtain the session features of the previous moment. According to the method, the customer service staff can be quickly allocated by classifying the session types. Meanwhile, keywords in the session are extracted, and quick response can be made to the meaning expressed by the customer, so that the customer s |
| format | Patent |
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The system comprises a first session collection module which is connected with a session classification module and is used for obtaining a current session text in real time and processing the current session text to obtain a current session feature; the second session collection module, the session classification module and the session text database are connected, and the second session collection module is used for obtaining the session text of the previous moment from the session text database and processing the session text of the previous moment to obtain the session features of the previous moment. According to the method, the customer service staff can be quickly allocated by classifying the session types. 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The system comprises a first session collection module which is connected with a session classification module and is used for obtaining a current session text in real time and processing the current session text to obtain a current session feature; the second session collection module, the session classification module and the session text database are connected, and the second session collection module is used for obtaining the session text of the previous moment from the session text database and processing the session text of the previous moment to obtain the session features of the previous moment. According to the method, the customer service staff can be quickly allocated by classifying the session types. 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The system comprises a first session collection module which is connected with a session classification module and is used for obtaining a current session text in real time and processing the current session text to obtain a current session feature; the second session collection module, the session classification module and the session text database are connected, and the second session collection module is used for obtaining the session text of the previous moment from the session text database and processing the session text of the previous moment to obtain the session features of the previous moment. According to the method, the customer service staff can be quickly allocated by classifying the session types. Meanwhile, keywords in the session are extracted, and quick response can be made to the meaning expressed by the customer, so that the customer s</abstract><oa>free_for_read</oa></addata></record> |
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| language | chi ; eng |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Customer service early warning analysis system based on deep learning |
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