CN115691557A - Intelligent customer service system, method, medium and equipment based on emotion recognition - Google Patents
Intelligent customer service system, method, medium and equipment based on emotion recognition Download PDFInfo
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Abstract
The present disclosure relates to an intelligent customer service system, method, device and medium based on emotion recognition, the system comprising: the voice data extraction module is used for extracting and storing the voice data of the user based on the conversation between the system and the user; the word segmentation processing module is used for carrying out word segmentation processing on the voice data to obtain word vector data; the entity identification module is used for extracting entity keywords in the word vector data and performing first sequencing on the word frequency of the entity keywords; the emotion recognition module is used for extracting emotion keywords in the word vector data and performing second sequencing according to the word frequency of the emotion keywords; a decision module for selecting a corresponding response strategy according to the first ordering and the second ordering based on a decision algorithm; and the voice module is used for generating a reply voice statement based on the response strategy. The system and the method disclosed by the invention can be used for identifying the emotion of the user, assisting in management decision and finally improving the service quality of the user.
Description
Technical Field
The present disclosure relates to the field of intelligent customer service technologies, and more particularly, to an intelligent customer service system, method, medium, and device based on emotion recognition.
Background
The online customer service system is a service system which supports multiple channels of conversation, and customer service personnel can reply uniformly on a platform and answer customers with problems in the using process.
With the gradual development, more and more customers will be provided, and then the number of customers of the online customer service will be increased, which is accompanied by the following problems:
the online seat service customers are busy, or the situations that the manpower of manual seats is insufficient, a large number of customers are in a queuing state and the like exist.
In order to solve the above problems, a powerful 24-hour online intelligent customer service is needed to replace the manual service of customers. However, the intelligent customer service in the prior art lacks the autonomous learning ability and cannot meet the requirements.
Disclosure of Invention
The intelligent customer service system aims to solve the technical problems that intelligent customer service in the prior art lacks autonomous learning ability and cannot meet requirements.
In order to achieve the above technical object, the present disclosure provides an intelligent customer service system based on emotion recognition, including:
the voice data extraction module is used for extracting and storing the voice data of the user based on the conversation between the system and the user;
the word segmentation processing module is used for carrying out word segmentation processing on the voice data to obtain word vector data;
the entity identification module is used for extracting entity keywords in the word vector data and performing first sequencing on the word frequency of the entity keywords;
the emotion recognition module is used for extracting emotion keywords in the word vector data and performing second sequencing according to the word frequency of the emotion keywords;
a decision module for selecting a corresponding response strategy according to the first ordering and the second ordering based on a decision algorithm;
and the voice module is used for generating a reply voice statement based on the response strategy.
Further, the emotion recognition module is specifically configured to:
extracting emotion keywords in the word vector data, and performing emotion keyword matching in a pre-constructed emotion word library;
correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
and carrying out third sequencing on the word frequency of the modified emotion keywords and replacing the result of the second sequencing with the third sequencing.
Further, the decision module is specifically configured to:
and selecting corresponding response strategies according to the first sequence and the second sequence based on a decision tree and a historical response decision strategy, and storing the response strategies.
Further, the decision module is specifically configured to:
judging whether the top entity key words in the first sequence and the top emotion key words in the second sequence can be found in historical response strategies, if so, selecting the corresponding historical response strategies as the current corresponding response strategies and storing the current response strategies;
and if not, performing keyword similarity matching on the entity keywords which are most front in the first sequence and the emotion keywords which are most front in the second sequence, and selecting three historical response strategies of which the similarity matching results are most front as the response strategies of the time.
Further, the word segmentation processing module is specifically configured to:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
In order to achieve the above technical object, the present disclosure can also provide an intelligent customer service response method based on emotion recognition, which is applied to the above intelligent customer service system based on emotion recognition, and includes:
extracting and storing voice data based on the system and the user's dialogue;
performing word segmentation processing on the voice data to obtain word vector data;
extracting entity keywords in the word vector data, and performing first sequencing on word frequencies of the entity keywords;
extracting emotion keywords in the word vector data, and performing second sequencing on word frequency of the emotion keywords;
based on the first sorting and the second sorting traversal history response strategy, selecting the history response strategy with the highest matching degree as the current response strategy;
and generating a reply voice sentence based on the current response strategy.
Further, after extracting emotion keywords in the word vector data and performing second sorting on word frequencies where the emotion keywords appear, the method further includes:
matching emotion keywords in a pre-constructed emotion word library;
correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
and carrying out third sequencing on the word frequency of the modified emotion keywords and replacing the result of the second sequencing with the third sequencing.
Further, the obtaining of word vector data by performing word segmentation processing on the voice data specifically includes:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
To achieve the above technical objects, the present disclosure can also provide a computer storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the above-mentioned alarm method.
In order to achieve the above technical object, the present disclosure further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above alarm method when executing the computer program.
The beneficial effect of this disclosure does:
1. by continuously giving more knowledge to the intelligent customer service, the identification capability of the service customer service of the intelligent customer service is improved, so that the working pressure of manual customer service can be relatively reduced, and more customers can have satisfactory service experience; and the intelligent customer service can monitor the emotion of the customer in real time, and can make a treatment measure for soothing the customer in time according to the emotion value of the customer, so that the risk of complaint of some customers is reduced, and the satisfaction degree of the customer is improved.
2. The service management can reflect the experience feeling of the subjective will of the client by analyzing the emotion corresponding to the voice bottom layer of the user, assist the management decision and finally improve the service quality of the user.
Drawings
Fig. 1 shows a schematic structural diagram of a system of embodiment 1 of the present disclosure;
figure 2 shows a flow diagram of a method of embodiment 2 of the present disclosure;
FIG. 3 shows a flow diagram of a method of embodiment 2 of the present disclosure;
fig. 4 shows a schematic structural diagram of embodiment 4 of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
Various structural schematics according to embodiments of the present disclosure are shown in the figures. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers, and relative sizes and positional relationships therebetween shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, as actually required.
The first embodiment is as follows:
as shown in fig. 1:
an intelligent customer service system 100 based on emotion recognition, comprising:
a voice data extraction module 101, configured to extract and store voice data of a user based on a dialog between the system and the user;
a word segmentation processing module 102, configured to perform word segmentation processing on the voice data to obtain word vector data;
the entity identification module 103 is configured to extract entity keywords in the word vector data, and perform a first ordering on word frequencies where the entity keywords appear;
the emotion recognition module 104 is used for extracting emotion keywords in the word vector data and performing second sequencing according to the word frequency of the emotion keywords;
a decision module 105, configured to select a corresponding response policy according to the first ordering and the second ordering based on a decision algorithm;
and the voice module 106 is used for generating a reply voice statement based on the response strategy.
The intelligent customer service system based on emotion recognition is different from the commonly used intelligent customer service in the prior art, not only can identify entity keywords in question sentences of conversation users, but also can carry out matching by the entity keywords, and further integrates an emotion recognition module, so that emotion recognition is carried out on the users who currently carry out conversation, and corresponding response strategies and response sentences are selected according to the current emotion of the users, so that the quality of service users can be better improved, and the user experience is also improved.
It should be noted that, the intelligent customer service system based on emotion recognition disclosed by the present disclosure respectively recognizes an entity keyword and an emotion keyword in a user question sentence of a user session.
For example: how can the balance in the payment instrument be extracted? "
The keywords of 'extract', 'pay' and 'balance' after word segmentation are all entity keywords associated with the essence of the problem to be solved;
and the keyword "how" contains the current emotion or emotion of the user.
Further, the emotion recognition module 104 is specifically configured to:
extracting emotion keywords in the word vector data, and performing emotion keyword matching in a pre-constructed emotion word library;
correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
and performing third sorting on the word frequency of the modified emotion keywords and replacing the second sorting result with the third sorting.
Through the further scheme, certain correction coefficients are given to certain emotion keywords capable of expressing strong emotions of the user, so that emotion recognition accuracy of the user who is currently in conversation is improved.
The emotion recognition module 104 of the present disclosure recognizes the current emotion of the user who performs the conversation, and if the recognized emotion keyword in the second ranking result or the corrected third ranking result indicates that the emotion of the user is a fallen emotion or an angry emotion keyword ranking is higher, some reply dialects for soothing the emotion of the user need to be fused in subsequent response strategies, or the user needs to be directly transferred to manual customer service to improve the service quality.
For example, the emotion keywords representing that the user's emotion is lost or angry in the identified second ranking result or the modified third ranking result are ranked higher, and some soothing words are selected, such as: "sorry, i will help you deal with as soon as possible" "" sorry, smart customer service will temporarily fail to answer your question, this will serve you to transfer to artificial customer service, will you see? ".
Further, the decision module 105 is specifically configured to:
and selecting corresponding response strategies according to the first sequence and the second sequence based on a decision tree and a historical response decision strategy, and storing the response strategies.
Based on the decision tree, the historical response strategy is combined to serve as the response strategy reference of the user session, and the user can continuously learn, improve and generate a better response strategy.
Further, the decision module 105 is specifically configured to:
judging whether the top entity key words in the first sequence and the top emotion key words in the second sequence can be found in historical response strategies, if so, selecting the corresponding historical response strategies as the current corresponding response strategies and storing the current response strategies;
and if not, performing keyword similarity matching on the entity keywords which are most front in the first sequence and the emotion keywords which are most front in the second sequence, and selecting three historical response strategies of which the similarity matching results are most front as the response strategies of the time.
Further, the word segmentation processing module 102 is specifically configured to:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
Conditional random fields (CRFs, or CRFs) are a discriminative probabilistic model of random fields, and are commonly used to label or analyze sequence data, such as natural language text or biological sequences. The conditional random field is a conditional probability distribution model P (Y | X) representing a markov random field of a set of output random variables Y given a set of input random variables X, i.e., the CRF is characterized by assuming that the output random variables constitute a markov random field. Conditional random fields can be viewed as a generalization of the maximum entropy markov model over the labeling problem.
Like a markov random field, a conditional random field is a graph model with no direction, in which the distribution of a random variable Y is a conditional probability and a given observation is a random variable X. In principle, the graph model layout of the conditional random field can be arbitrarily given, and a general layout is a chained architecture, which has a more efficient algorithm for calculation whether in training (training), inference (inference), or decoding (decoding). The conditional random field is a typical discriminant model, and the joint probability thereof can be written in the form of multiplication of several potential functions, wherein the most common is the linear chain element random field.
For example, if the obtained voice information is "i go to bank to handle business", the division processing is performed on the name of the business enterprise, and a plurality of divisions constituting the name of the business enterprise are "i", "go", "bank", "handle" and "business", respectively.
Optionally, the word segmentation processing module of the present disclosure uses a rule-based word segmentation algorithm, and performs word delimitation by combining with analysis of the information provided above, where the core of the word segmentation algorithm is a bidirectional maximum matching method, that is, both forward and backward traverse are performed once, and then a certain word segmentation result is selected and output according to the principle that more large-granularity words are better, and fewer non-dictionary words and single words are better.
The system disclosed by the invention continuously endows more knowledge to the intelligent customer service, so that the identification capability of the service customer service of the intelligent customer service is improved, the working pressure of manual customer service can be relatively reduced, and more customers can have satisfactory service experience; and the intelligent customer service can monitor the emotion of the customer in real time, and can make a treatment measure for soothing the customer in time according to the emotion value of the customer, so that the risk of complaint of some customers is reduced, and the satisfaction degree of the customer is improved. The service management of the system can reflect the experience feeling of the subjective will of the client by analyzing the emotion corresponding to the bottom layer of the user sound, can assist the management decision and finally improves the service quality of the user.
The system disclosed by the invention counts top (n) words with the highest frequency in one day, and gives the corresponding customer acoustic questions and answers to the words to the intelligent customer service. And training intelligent customer service through a machine learning prediction modeling algorithm such as a decision tree, learning a standard question method in the language number of the words with the highest occurrence frequency, and intelligently matching answers of the customer service corresponding to the question method.
Example two:
as shown in fig. 2, to achieve the above technical object, the present disclosure can also provide an intelligent customer service response method based on emotion recognition, which is applied in the above intelligent customer service system 100 based on emotion recognition, and includes:
s201: extracting and storing voice data based on the system and the user's dialogue;
s202: performing word segmentation processing on the voice data to obtain word vector data;
s203: extracting entity keywords in the word vector data, and performing first sequencing on word frequencies of the entity keywords;
s204: extracting emotion keywords in the word vector data, and performing second sequencing on word frequency of the emotion keywords;
s205: based on the first sorting and the second sorting traversal history response strategy, selecting the history response strategy with the highest matching degree as the current response strategy;
s206: and generating a reply voice sentence based on the current response strategy.
As shown in fig. 3:
further, after extracting emotion keywords in the word vector data and performing second sorting on word frequencies where the emotion keywords appear, the method further includes:
s2041: matching emotion keywords in a pre-constructed emotion word library;
s2042: correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
s2043: and carrying out third sequencing on the word frequency of the modified emotion keywords and replacing the result of the second sequencing with the third sequencing.
Further, the obtaining of word vector data by performing word segmentation processing on the voice data specifically includes:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
Optionally, the word segmentation processing process of the present disclosure uses a rule-based word segmentation algorithm, and combines with analysis of the information provided above to delimit words, the core of the word segmentation algorithm is a bidirectional maximum matching method, that is, both forward and backward traverse one time, and then selects one point of the word segmentation results to output according to the principle that more large-granularity words are better, and less non-dictionary words and single words are better.
The disclosed method continuously endows more knowledge to the intelligent customer service, so that the identification capability of the service customer service of the intelligent customer service is improved, the working pressure of manual customer service can be relatively reduced, and more customers can have satisfactory service experience; and the intelligent customer service can monitor the emotion of the customer in real time, and can make a treatment measure for soothing the customer in time according to the emotion value of the customer, so that the risk of complaint of some customers is reduced, and the satisfaction degree of the customer is improved. The service management of the method can reflect the experience feeling of the subjective will of the client by analyzing the emotion corresponding to the bottom layer of the user sound, can assist the management decision and finally improves the service quality of the user.
According to the method, top (n) words with the highest frequency in one day are counted, and the original voice questions and answers of the clients corresponding to the words are endowed to intelligent customer service. And training intelligent customer service through a machine learning prediction modeling algorithm such as a decision tree, learning a standard question method in the language number of the words with the highest occurrence frequency, and intelligently matching answers of the customer service corresponding to the question method.
Example three:
the present disclosure can also provide a computer storage medium having stored thereon a computer program for implementing the steps of the above-described intelligent customer service response method based on emotion recognition when the computer program is executed by a processor.
The computer storage medium of the present disclosure may be implemented with a semiconductor memory, a magnetic core memory, a magnetic drum memory, or a magnetic disk memory.
Semiconductor memories are mainly used as semiconductor memory elements of computers, and there are two types, mos and bipolar memory elements. Mos devices have high integration, simple process, but slow speed. The bipolar element has the advantages of complex process, high power consumption, low integration level and high speed. NMos and CMos were introduced to make Mos memory dominate in semiconductor memory. NMos is fast, e.g. 45ns for 1K bit sram from intel. The CMos power consumption is low, and the access time of the 4K-bit CMos static memory is 300ns. The semiconductor memories described above are all Random Access Memories (RAMs), i.e. read and write new contents randomly during operation. And a semiconductor Read Only Memory (ROM), which can be read out randomly but cannot be written in during operation, is used to store solidified programs and data. The ROM is classified into a non-rewritable fuse type ROM, PROM, and a rewritable EPROM.
The magnetic core memory has the characteristics of low cost and high reliability, and has more than 20 years of practical use experience. Magnetic core memories were widely used as main memories before the mid 70's. The storage capacity can reach more than 10 bits, and the access time is 300ns at the fastest speed. The international typical magnetic core memory capacity is 4 MS-8 MB, and the access cycle is 1.0-1.5 mus. After semiconductor memory is rapidly developed to replace magnetic core memory as a main memory location, magnetic core memory can still be applied as a large capacity expansion memory.
Drum memory, an external memory for magnetic recording. Because of its fast information access speed and stable and reliable operation, it is being replaced by disk memory, but it is still used as external memory for real-time process control computers and medium and large computers. In order to meet the needs of small and micro computers, subminiature magnetic drums have emerged, which are small, lightweight, highly reliable, and convenient to use.
Magnetic disk memory, an external memory for magnetic recording. It combines the advantages of drum and tape storage, i.e. its storage capacity is larger than that of drum, its access speed is faster than that of tape storage, and it can be stored off-line, so that the magnetic disk is widely used as large-capacity external storage in various computer systems. Magnetic disks are generally classified into two main categories, hard disks and floppy disk memories.
Hard disk memories are of a wide variety. The structure is divided into a replaceable type and a fixed type. The replaceable disk is replaceable and the fixed disk is fixed. The replaceable and fixed magnetic disks have both multi-disk combinations and single-chip structures, and are divided into fixed head types and movable head types. The fixed head type magnetic disk has a small capacity, a low recording density, a high access speed, and a high cost. The movable head type magnetic disk has a high recording density (up to 1000 to 6250 bits/inch) and thus a large capacity, but has a low access speed compared with a fixed head magnetic disk. The storage capacity of a magnetic disk product can reach several hundred megabytes with a bit density of 6 bits per inch and a track density of 475 tracks per inch. The disk set of the multiple replaceable disk memory can be replaced, so that the disk set has large off-body capacity, large capacity and high speed, can store large-capacity information data, and is widely applied to an online information retrieval system and a database management system.
Example four:
the present disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the intelligent customer service response method based on emotion recognition when executing the computer program.
Fig. 4 is a schematic diagram of an internal structure of the electronic device in one embodiment. As shown in fig. 4, the electronic device includes a processor, a storage medium, a memory, and a network interface connected through a system bus. The storage medium of the computer device stores an operating system, a database and computer readable instructions, the database can store control information sequences, and the computer readable instructions can make the processor realize a communication method when being executed by the processor. The processor of the electrical device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may have computer readable instructions stored thereon that, when executed by the processor, cause the processor to perform a method for intelligent customer service response based on emotion recognition. The network interface of the computer device is used for connecting and communicating with the terminal. Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The electronic device includes, but is not limited to, a smart phone, a computer, a tablet, a wearable smart device, an artificial smart device, a mobile power source, and the like.
The processor may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing remote data reading and writing programs, etc.) stored in the memory and calling data stored in the memory.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory and at least one processor or the like.
Fig. 4 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 4 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the electronic device may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the disclosure, and these alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (10)
1. An intelligent customer service system based on emotion recognition, comprising:
the voice data extraction module is used for extracting and storing the voice data of the user based on the conversation between the system and the user;
the word segmentation processing module is used for carrying out word segmentation processing on the voice data to obtain word vector data;
the entity identification module is used for extracting entity keywords in the word vector data and performing first sequencing on the word frequency of the entity keywords;
the emotion recognition module is used for extracting emotion keywords in the word vector data and performing second sequencing according to the word frequency of the emotion keywords;
a decision module for selecting a corresponding response strategy according to the first ordering and the second ordering based on a decision algorithm;
and the voice module is used for generating a reply voice statement based on the response strategy.
2. The system of claim 1, wherein the emotion recognition module is specifically configured to:
extracting emotion keywords in the word vector data, and performing emotion keyword matching in a pre-constructed emotion word library;
correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
and carrying out third sequencing on the word frequency of the modified emotion keywords and replacing the result of the second sequencing with the third sequencing.
3. The system of claim 1, wherein the decision module is specifically configured to:
and selecting corresponding response strategies according to the first sequence and the second sequence based on a decision tree and a historical response decision strategy, and storing the response strategies.
4. The system of claim 3, wherein the decision module is specifically configured to:
judging whether the top entity key words in the first sequence and the top emotion key words in the second sequence can be found in historical response strategies, if so, selecting the corresponding historical response strategies as the current corresponding response strategies and storing the current response strategies;
and if not, performing keyword similarity matching on the entity keywords which are most front in the first sequence and the emotion keywords which are most front in the second sequence, and selecting three historical response strategies of which the similarity matching results are most front as the response strategies of the time.
5. The system according to any one of claims 1 to 4, wherein the segmentation processing module is specifically configured to:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
6. An intelligent customer service response method based on emotion recognition, which is applied to the intelligent customer service system based on emotion recognition as claimed in any one of claims 1-5, and comprises:
extracting and storing voice data based on the system and the user's dialogue;
performing word segmentation processing on the voice data to obtain word vector data;
extracting entity keywords in the word vector data, and performing first sequencing on word frequencies of the entity keywords;
extracting emotion keywords in the word vector data, and performing second sequencing on word frequency of the emotion keywords;
based on the first sorting and the second sorting, traversing the historical response strategies, and selecting the historical response strategy with the highest matching degree as the current response strategy;
and generating a reply voice sentence based on the current response strategy.
7. The method of claim 6, wherein after extracting emotion keywords in the word vector data and performing the second ordering of word frequencies at which the emotion keywords occur, the method further comprises:
matching emotion keywords in a pre-constructed emotion word library;
correcting the word frequency of the emotion keywords based on the word frequency correction coefficient of the emotion keywords preset in the emotion word library;
and carrying out third sequencing on the word frequency of the modified emotion keywords and replacing the result of the second sequencing with the third sequencing.
8. The method according to claim 6 or 7, wherein the obtaining of word vector data by performing word segmentation processing on the speech data specifically includes:
and performing word segmentation processing on the voice data based on the conditional random field to obtain word vector data.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps corresponding to the intelligent customer service response method based on emotion recognition as claimed in any one of claims 5 to 8 when executing the computer program.
10. A computer storage medium having stored thereon computer program instructions, wherein the program instructions, when executed by a processor, are adapted to carry out the steps corresponding to the intelligent emotion recognition based customer service response method as claimed in any one of claims 5 to 8.
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| CN117726301A (en) * | 2023-12-26 | 2024-03-19 | 重庆不贰科技(集团)有限公司 | Intelligent decision-making system based on production line management and Chat combined model |
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