CN106529804A - Client complaint early-warning monitoring analyzing method based on text mining technology - Google Patents
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Abstract
The invention discloses a client complaint early-warning monitoring analyzing method based on text mining technology. The method comprises the steps of a text data standardizing step for converting recorded text data into data with a standard data format in a unified rule; and a standard data analysis early-warning step, analyzing the standard data format through establishing a complaint analysis grade clustering model, determining a risk grade according to the clustering result, and transmitting early-warning according to the risk grade. The complaint early-warning monitoring analyzing method satisfies a precondition of ensuring client satisfaction degree and furthermore has advantages of greatly reducing workload in manual sampling observation, effectively improving passive after-event tracking responsibility-determining management mode and facilitating before-event active server in a targeted manner, thereby realizing a professional management requirement for in-time response for client demands.
Description
Technical field
The present invention relates to the customer complaint early warning and monitoring analysis method based on Text Mining Technology, art is electric power row
Industry client's crisis management field.
Background technology
With the progressively in-depth of power system reform, electric power sale market competition aggravation, in the urgent need to power supply enterprise is rapid
Change traditional mode of thinking and mode of operation, further establish market-oriented service awareness, Business Innovation service mode is lifted
Customization, personalized service level, the trust of Win Clients, it is ensured that the market share.Meanwhile, with common people's sense of independence and right-safeguarding
The continuous lifting of consciousness, power supply enterprise's bidding price adjustment each time, service entries change even breakdown repair, all receives vast
The close attention of the common people.
Used as the important window with customer communication, communication, 95598 customer service systems have recorded the customer information of magnanimity.
Data in the system are broadly divided into structural data and unstructured data.At present, for the structural data in system, it is
Unite by complaining the aspects such as quantity, the satisfaction marking of client or issue handling timeliness to carry out statistical analysiss.It is anti-for client
Feedforward information unstructured data mainly manually to inspect by random samples based on combing, ask by the grasp client's focus of attention being not easy to promptly and accurately
Topic；And be only limitted to carry out statistical analysiss from dimensions such as time, region, types of service so that what monitoring was analyzed becomes more meticulous degree not
It is enough；Although text data to complaining work order carries out artificial Source Tracing one by one simultaneously, lacks automatic monitoring analysis, and side
Overweight and call to account afterwards, it is impossible to realize early warning in advance.
The content of the invention
The technical problem to be solved is to overcome prior art not enough, there is provided a kind of to be based on Text Mining Technology
Complaint early-warning monitoring method, can in time, accurately grasp client feedback hot issue for departments that are in charge of manging enterprises, and in advance
The requirement of early warning customer complaint risk, throws to client in client feedback information numerous and complicated, expression way lower realization versatile and flexible
The early warning of risk is told, solves the problems, such as that at present artificial carding efficiency is low and the Passive Management called to account of tracing to the source afterwards, so as to protect
The complaint risk grade forecast to every work order is demonstrate,proved, has been easy to carry out in time and is taken the initiative in offering a hand, improve CSAT.
For solving above-mentioned technical problem, the technical solution used in the present invention is：
A kind of complaint early warning and monitoring analysis method based on Text Mining Technology, including：
The text data of typing is changed into the normalized number of uniform rules according to mould by step 1, text data standardizing step
Formula；
Step 2, normalized number complain analytical grade Clustering Model to normalized number evidence according to analysis and early warning step by setting up
Pattern is analyzed, and divides complaint risk grade according to cluster result, sends corresponding early warning further according to risk place grade.
Text data is converted into by structuring, standardized standardization expression formula using text data standardizing step, just
Process in the later stage and apply；Code requirement data analysiss warning step realizes the judgement and early warning to customer complaint risk class,
Artificial combing and differentiation is saved, work efficiency is effectively increased.
Used as the further limits scheme of the present invention, text data standardizing step includes：
Step 1.1, text initial processing step carry out participle and denoising to the text data of typing, obtain each
Key word in text data；
Each key word is carried out vectorization and makees normalized by step 1.2, Text eigenvector step, is obtained
By each key word WiIn file djIn vectorization normalization result set up real-valued matrix, key word WiIn file djIn
Vectorization normalization result be：
In formula：N represents total number of files amount；NiRepresent comprising key word WiQuantity of documents；N is key word total degree；WiFor
I-th key word；djRepresent j-th file；tfijFor key word WiIn file djIn word frequency；For relevant
Keyword is in file djIn word frequency quadratic sum；For total number of files amount and comprising key word WiThe quantity of file
Ratio is taken the logarithm after adding adjustment item 0.01；
Step 1.3, text data similarity matching step, according to the real-valued matrix set up, are calculated using the cosine law
Cosine similarity between each key word, and COS distance nearest text data is matched, form near synonym dictionary；
Step 1.4, normalized number according to generation step, by the text data for matching according to setting unified standard pattern
Generate normalized number evidence.
Used as the further limits scheme of the present invention, normalized number includes according to analysis and early warning step：
Step 2.1, Sentiment orientation degree calculation procedure carry out Judgment by emotion and are divided into positive, passive to normalized number evidence
With three class of center；
Step 2.2, complaint risk grade classification step, according to customer grade, actively type of service, tendency degree and complaint
History parameters are set up and complain analytical grade Clustering Model, and formulate complaint risk hierarchy rules according to cluster result；
Step 2.3, complain analysis and early warning step, according to formulate complaint risk hierarchy rules, preference pattern variable parameter,
Classification learning model is set up using Bayes's classification, the throwing to unknown text data is realized by the training to classification learning model
Tell the prediction of risk class.
Used as the further limits scheme of the present invention, the specific works step of Sentiment orientation degree calculation procedure is：
(1) conjunction and negative word dictionary are set up；
(2) conjunction and negative word, and labelling corresponding words are extracted according in from normalized number according to conjunction and negative word dictionary
In position of the normalized number according in；
(3) existing sentiment dictionary storehouse is matched, obtains polarity and its emotion score value of vocabulary；
(4) by conjunction position, determine front sentence and rear sentence proportion, further according to negative word position judgment double denial with
And the polarity inversion of neighbouring vocabulary；
(5) using vocabulary polarity and its emotion score value bring conjunction and negative word into after to normalized number according to adding up
Obtain affection computation scoring；
(6) circulation step (2) to (5), if affection computation scoring be positive for canonical, are that to bear be then passive, otherwise in
The heart.
As the further limits scheme of the present invention, complaint risk hierarchy rules for according to cluster result by complaint risk
Grade classification is high-risk pole, hazard class, has complaint tendency level, general level and without this five risk class of complaint tendency level.
As the further limits scheme of the present invention, need to verify rule complaint risk hierarchy rules are formulated,
The classification learning model that checking collection data input is set up, obtains the complaint risk grade of each text data in checking collection data,
And result set is compared with corresponding level data in checking collection data, the accuracy of computation model prediction.
The beneficial effects of the present invention is：Using Text Mining Technology, text data is converted into into structuring, standardized
Standardization expression formula；Secondly sentiment analysis are carried out to the text message of client feedback, and calculates its Sentiment orientation degree；Finally utilize
There is the classification learning algorithm of supervision, set up and complain early warning and monitoring model, realize the judgement and early warning to customer complaint risk class.
Description of the drawings
Fig. 1 is standardization expression formula Establishing process schematic diagram；
Fig. 2 is complaint risk rating calculation flow chart proposed by the present invention；
Fig. 3 is complaint Early-warning Model establishment step schematic diagram proposed by the present invention；
Fig. 4 is complaint Early-warning Model checking schematic flow sheet proposed by the present invention；
Fig. 5 is each region work order number scattergram；
Fig. 6 is each grade work order data profile.
Specific embodiment
Below in conjunction with the accompanying drawings, the present invention is elaborated：
A kind of complaint early warning and monitoring analysis method based on Text Mining Technology of the present invention, walks including text data standardization
, according to analysis and early warning step, wherein, text data standardizing step, for changing into system by the text data of typing for rapid and normalized number
The standardization data pattern of one rule；Normalized number complains analytical grade to cluster mould according to analysis and early warning step for passing through to set up
Type is analyzed to data pattern of standardizing, and is divided complaint risk grade according to cluster result, is sent according to risk place grade
Corresponding early warning.
Text data is converted into by structuring, standardized standardization expression formula using text data standardizing step, just
Process in the later stage and apply；Code requirement data analysiss warning step realizes the judgement and early warning to customer complaint risk class,
Artificial combing and differentiation is saved, work efficiency is effectively increased.
Text data standardizing step includes text initial processing step, Text eigenvector step, text data phase
Like property matching step and normalized number according to generation step.The concrete process step of text data standardizing step as shown in figure 1,
The text data (95598 work order data) of typing is carried out into participle, denoising first；Then carry out vectorization and be processed into vector
Matrix, word correlation matrix；Then it is associated analysis and generates near synonym dictionary；According to type of service and near synonym dictionary to text
Data carry out standardization expression.
Wherein, text initial processing step, carries out participle and denoising for the text data to typing, obtains each
Key word in text data；
Text eigenvector step, for each key word being carried out vectorization and making normalized, obtains by each
Individual key word WiIn file djIn vectorization normalization result set up real-valued matrix, key word WiIn file djIn to
Quantifying normalization result is：
In formula：N represents total number of files amount；NiRepresent comprising key word WiQuantity of documents；N is key word total degree；WiFor
I-th key word；djRepresent j-th file；tfijFor key word WiIn file djIn word frequency；For relevant
Keyword is in file djIn word frequency quadratic sum；For total number of files amount and comprising key word WiThe quantity of file
Ratio is taken the logarithm after adding adjustment item 0.01；
Text data similarity matching step, for according to the real-valued matrix set up, calculating each pass using the cosine law
Cosine similarity between keyword, and COS distance nearest text data is matched, form near synonym dictionary；
Normalized number according to generation step, for by the text data for matching according to setting unified standard schema creation
Normalized number evidence.
Scheme is further disclosed as the present invention, and normalized number includes according to analysis and early warning step：
Sentiment orientation degree calculation procedure, for normalized number according to carry out Judgment by emotion and be divided into it is positive, passive and in
Three class of the heart；
Complaint risk grade classification step, for according to customer grade, actively type of service, tendency degree and complaint history
Parameter is set up and complains analytical grade Clustering Model, and formulates complaint risk hierarchy rules according to cluster result；Wind is complained formulating
Dangerous hierarchy rules need to verify rule that the classification learning model for setting up checking collection data input obtains checking collection number
The complaint risk grade of each text data according in, and result set is compared with corresponding level data in checking collection data,
The accuracy of computation model prediction；
Complain analysis and early warning step, for according to formulate complaint risk hierarchy rules, preference pattern variable parameter (if any
Without complaint history, type of service and customer grade), classification learning model is set up using Bayes's classification, by taxonomy
The prediction of the complaint risk grade to unknown text data is realized in the training for practising model.
The present invention is based on concretely comprising the following steps that the complaint early warning and monitoring analysis system of Text Mining Technology is realized：
(1) set up standardization expression formula
1st, Chinese word segmentation
Based on 95598 work order data, set up participle corpus and special symbol table, to expect storehouse in adjacent co-occurrence each
The frequency of combinatorics on words is counted, and calculates its degree of association.Computing formula is as follows：
Wherein, M represents expectation storehouse number of words, NARepresent the number of times that A occurs in storehouse is expected, NBRepresent that B occurs in storehouse is expected
Number of times, NABRepresent the number of times that AB occurs in storehouse is expected together.
2nd, Text eigenvector
A part of maximally efficient feature is selected from 95598 work order content of text so that the dimension in new feature space
Luv space dimension is often much smaller than, the further purification to Text eigenvector is realized, and is being kept the feelings of original text implication
Under condition, content of text, and succinct characteristic vector can be most fed back in calculating.By doing normalized to text feature item, mitigate
Impact of the different length text to Text similarity computing result.Computing formula is as follows：
In formula：N represents total number of files amount；NiRepresent comprising key word WiQuantity of documents；N is key word total degree；WiFor
I-th key word；djRepresent j-th file；tfijFor key word WiIn file djIn word frequency；For relevant
Keyword is in file djIn word frequency quadratic sum；For total number of files amount and comprising key word WiThe quantity of file
Ratio is taken the logarithm after adding adjustment item 0.01.
3rd, according to the real-valued matrix after term vector conversion, using the cosine law, the cosine calculated between each phrase is similar
Degree, and COS distance nearest word is matched, form near synonym dictionary.
4th, set up standardization expression formula
Classify with reference to 95598 system business, realize the canonical representation to client feedback text message, shape is such as：Complain-super
Cui personnel-attitude.
(2) set up and complain early warning and monitoring model
1st, calculate Sentiment orientation degree
(1) Dalian University of Technology's sentiment dictionary is based on, using 95598 work order classs of service as praising text to repair as supplement
Change, while setting up conjunction and negative word dictionary, set up emotion training storehouse.
(2) extraction conjunction and negative word from 95598 work orders, and labelling corresponding words position.
(3) sentiment dictionary is matched, determines that vocabulary polarity and its emotion score value, vocabulary polarity and emotion score value are emotion
Existing basic parameter in dictionary.
(4) by conjunction position, front sentence and rear sentence proportion are determined, while dual no according to the interpretation of negative word position
Determine, and neighbour enters the polarity inversion of vocabulary.
(5) the work order text emotion that adds up calculates scoring.
(6) circulation step (2) is to (5), if canonical is positive, be it is negative be then passiveness, otherwise centered on.
2nd, formulate complaint risk grade
By work order text data is converted into vectorization matrix, selection customer grade, actively tendency degree, complaint history etc.
Parameter, set up complain analytical grade Clustering Model, finally according to cluster result, by complaint risk grade classification be 5 classes, such as Fig. 2
It is shown.
3rd, set up and complain analysis and early warning model
Data source is divided into into model training collection and checking collection, and according to the complaint risk hierarchy rules formulated, preference pattern
Variable (such as whether there is complaint history, type of service, customer grade etc.), sets up classification learning model using Bayes's classification, passes through
The step of model training realizes the prediction of the complaint risk grade to unknown work order, model training is as shown in Figure 3.
4th, model checking
As shown in figure 4, by checking collection data input classification learning model, by complaint risk of each work order of model prediction etc.
Level, and the data that result set is concentrated with checking are compared, the accuracy of computation model prediction.
Embodiment
Collect 95598 system Nanjing August part work order data, 87359 altogether.Including business consultation, troublshooting, build
The data of the nine class work order such as view, suggestion, complaint, report.Wherein Nanjing City produces work order up to 58151, is secondly river
Peaceful area is 13248, and each region work order number distribution refers to Fig. 5.
1st, August part take the initiative in offering a hand grade work order monitoring analysis
Calculated by model, August part Nanjing work order is divided into into five classes and is taken the initiative in offering a hand grade.Wherein one-level work order is 232
Bar, two grades of work orders are 208, and as one-level, two grades of work orders are taken the initiative in offering a hand higher ranked, needs are processed in time, so
Following selective analysiss one-level, two grades of work orders distribution situation and model checking monitoring analysis in each region.Each grade work order number
Fig. 6 is seen according to distribution details：
(1) monitoring of each grade work order in region is analyzed
According to model monitoring result, Nanjing City one-level, two grades of work order quantity are at most respectively 109 and 90；Secondly
66 and 62 are respectively for Jiangning District；Pukou, the six directions, Lishui are relatively fewer, and wherein Pukou is respectively 20,24, the six directions point
Not Wei 14,14, Lishui area be respectively 14,10；Gaochun area is at least respectively 9,8.Business of each grade work order in region
Following table is referred to distributed number：
(2) one-level, two grades of work order content analysis
According to model monitoring result, each regional level work order content top ranked for reporting for repairment-without electricity, accounting is respectively river
26% is accounted for rather, Pukou accounts for 28%, and urban district accounts for 22%, and the six directions accounts for 31%, and Gaochun accounts for 26%, and Lishui accounts for 31%；
One-level work order content be number two for equipment-failure and reporting-many families-for repairment without electricity, wherein Jiangning, Pukou and Nanjing City ranking
Second content is equipment-failure, accounts for 16%, 18% and 21% respectively, and the content that the six directions, Gaochun and Lishui are number two is report
Xiu-many families-and without electricity, 18%, 19% and 25% is accounted for respectively.Accounting ranking and first etc. of two grades of work order contents in each region
Level is similar.
2nd, model pre-warning monitoring analysis
In monitoring cycle, using Early-warning Model of taking the initiative in offering a hand, common early warning one-level, two grades of work orders, 440 (complaint class work orders
128, non-complaint class work order 312).Find in one-level, two grades of non-complaint class work orders there are 35 finally to cause through checking
Customer complaint, wherein has 13 work orders (one-level work order 7, two grades work order 6) directly to cause customer complaint, main business type
It is troublshooting 5, service request 3, business consultation 5；Remaining 22 (one-level work order 12, two grades work order 10) work order
It is that customer complaint is finally caused by association, totally 9.In this 35 work orders, business consultation accounting is up to about 54%, secondly
It is troublshooting and service request, respectively may be about 34%, 12%.
Analyzed by the monitoring to model pre-warning result, in 128 for actually occurring complain work order, there are 106 to be visitor
Directly complained in the case of no historical behavior at family.Remaining work order be client have before complaint corresponding troublshooting,
The historical behaviors such as business consultation, and be integrally incorporated in this monitoring result.Model is demonstrated to non-complaint class in monitoring cycle
Work order later transformation is the capture ability for complaining work order.
Using Early-warning Model is complained, in time capture complaint risk degree compared with work order, be easy to business department to carry out in time actively
Service, so as to reduce complaining work order data, lifts the satisfaction of client.
Claims (6)
1. a kind of complaint early warning and monitoring analysis method based on Text Mining Technology, it is characterised in that include：
The text data of typing is changed into the standardization data pattern of uniform rules by step 1, text data standardizing step；
Step 2, normalized number complain analytical grade Clustering Model to data pattern of standardizing according to analysis and early warning step by setting up
Be analyzed, complaint risk grade is divided according to cluster result, corresponding early warning is sent further according to risk place grade.
2. the complaint early warning and monitoring analysis method based on Text Mining Technology according to claim 1, it is characterised in that text
Notebook data standardizing step includes：
Step 1.1, text initial processing step carry out participle and denoising to the text data of typing, obtain each text
Key word in data；
Each key word is carried out vectorization and makees normalized by step 1.2, Text eigenvector step, is obtained by each
Individual key word WiIn file djIn vectorization normalization result set up real-valued matrix, key word WiIn file djIn to
Quantifying normalization result is：
In formula：N represents total number of files amount；NiRepresent comprising key word WiQuantity of documents；N is key word total degree；WiFor i-th
Individual key word；djRepresent j-th file；tfijFor key word WiIn file djIn word frequency；For all key words
In file djIn word frequency quadratic sum；For total number of files amount and comprising key word WiThe quantity ratio of file
Plus adjustment item 0.01 after take the logarithm；
Step 1.3, text data similarity matching step, according to the real-valued matrix set up, calculate each pass using the cosine law
Cosine similarity between keyword, and COS distance nearest text data is matched, form near synonym dictionary；
Step 1.4, normalized number according to generation step, by the text data for matching according to setting unified standard schema creation
Normalized number evidence.
3. the complaint early warning and monitoring analysis method based on Text Mining Technology according to claim 2, it is characterised in that rule
Generalized data analysiss warning step includes：
Step 2.1, Sentiment orientation degree calculation procedure, to normalized number according to carry out Judgment by emotion and be divided into it is positive, passive and in
Three class of the heart；
Step 2.2, complaint risk grade classification step, according to customer grade, actively type of service, tendency degree and complaint history
Parameter is set up and complains analytical grade Clustering Model, and formulates complaint risk hierarchy rules according to cluster result；
Step 2.3, complains analysis and early warning step, according to the complaint risk hierarchy rules formulated, preference pattern variable parameter, utilizes
Bayes's classification sets up classification learning model, realizes the complaint wind to unknown text data by the training to classification learning model
The prediction of dangerous grade.
4. the complaint early warning and monitoring analysis method based on Text Mining Technology according to claim 3, it is characterised in that feelings
Sense tendency degree calculation procedure specific works step be：
(1) conjunction and negative word dictionary are set up；
(2) conjunction and negative word, and labelling corresponding words are extracted on rule according in from normalized number according to conjunction and negative word dictionary
Position in generalized data；
(3) existing sentiment dictionary storehouse is matched, obtains polarity and its emotion score value of vocabulary；
(4) by conjunction position, front sentence and rear sentence proportion are determined, further according to negative word position judgment double denial and neighbour
The polarity inversion of nearly vocabulary；
(5) using vocabulary polarity and its emotion score value bring conjunction and negative word into after to normalized number according to carrying out cumulative acquisition
Affection computation scores；
(6) circulation step (2) to (5), if affection computation scoring be positive for canonical, be bear be then it is passive, otherwise centered on.
5. the complaint early warning and monitoring analysis method based on Text Mining Technology according to claim 3, it is characterised in that throw
Tell risk class rule for according to cluster result by complaint risk grade classification be high-risk pole, hazard class, have complaint tendency level,
General level and without complaining tendency level this five risk class.
6. the complaint early warning and monitoring analysis method based on Text Mining Technology according to claim 3, it is characterised in that
Formulating complaint risk hierarchy rules needs to verify rule, the classification learning model that checking collection data input is set up, and obtains
The complaint risk grade of each text data in collection data must be verified, and result set and checking are collected into corresponding level data in data
Compare, the accuracy of computation model prediction.
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| CN107194568A (en) * | 2017-05-18 | 2017-09-22 | 广东中科国志科技发展有限公司 | A kind of method and apparatus of analytical technology |
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