CN117764657A - Network complaint prediction method and system based on Monte Carlo method - Google Patents
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Abstract
The invention discloses a network complaint prediction method and a system based on a Monte Carlo method, belongs to the technical field of big data processing, and aims to solve the technical problem of how to improve complaint processing efficiency and accuracy of processing results. The method comprises the following steps: collecting network complaint data and constructing a complaint data set; extracting valid features from the complaint dataset; selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features; constructing a network complaint prediction model based on a Monte Carlo method, wherein the network complaint prediction model is used for predicting and classifying complaints in a future preset time by taking complaint data as input; and predicting and classifying complaints in a preset future time period through the current trained network complaint prediction model by taking the current network complaint data as input, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a network complaint prediction method and system based on a Monte Carlo method.
Background
With the popularity and application of the internet, network complaints have become a common way of communication between businesses and users. However, the number of network complaints is huge, and how to efficiently process and accurately predict the network complaints becomes a technical problem to be solved.
Currently, research for network complaint treatment mainly includes two aspects. On one hand, researchers aim at utilizing technologies such as data mining, natural language processing, machine learning and the like to realize rapid classification and identification of network complaints, and improve the efficiency and accuracy of complaint processing. On the other hand, researchers construct a network complaint quantity prediction model through analysis and prediction of historical complaint data, so that prediction and trend analysis of future complaint quantity are realized, and decision support is provided for enterprises.
Aiming at classification and identification problems in complaint treatment, researchers construct classification models by using machine learning algorithms and perform emotion analysis based on text feature extraction technology. For example, algorithms such as a support vector machine, naive Bayes, deep learning and the like are utilized to realize classification and emotion analysis of complaint texts, and accuracy and efficiency of complaint processing are improved. And simultaneously, researchers also use natural language processing technology to construct a semantic analysis model so as to realize deep analysis and understanding of complaint information.
Researchers have proposed various methods and techniques for complaint prediction. The method based on time series analysis is a more common method, and the trend and period of the historical complaint data are analyzed to predict the change trend of the future complaint number. In addition, prediction methods based on algorithms such as regression analysis, neural networks, decision trees and the like are also widely applied to the field of network complaint prediction. Recently, a complaint quantity prediction method based on a Monte Carlo technology also receives attention of researchers, and a Monte Carlo model is established through analysis and simulation of historical complaint data so as to predict distribution and trend of future complaint quantity and provide decision support for enterprises.
In addition, technical research on network complaint treatment also relates to the directions of data visualization, intelligent customer service, automatic treatment and the like. By applying the data visualization technology to the display and analysis of the network complaint data, researchers can more intuitively understand the complaint conditions and trends; by utilizing the intelligent customer service technology, the automatic processing and intelligent reply of complaint information can be realized, and the complaint processing efficiency and the user satisfaction of enterprises are improved. Meanwhile, the automatic processing technology can also be applied to automatic arrangement and classification of complaint data, and the efficiency and accuracy of complaint processing are further improved.
In practical applications, network complaint handling techniques have been widely used in various industries. For example, enterprises such as telecom operators, electronic commerce platforms, financial institutions and the like face massive complaint data, and the quick classification and accurate identification of the data can be realized by utilizing a network complaint processing technology, so that the efficiency and quality of complaint processing are improved. Meanwhile, by utilizing the network complaint prediction technology, the enterprise can more accurately predict the change trend of the future complaint quantity, and more reliable support is provided for enterprise decision-making.
How to improve the complaint processing efficiency and the accuracy of the processing result is a technical problem to be solved.
Disclosure of Invention
The technical task of the invention is to provide a network complaint prediction method and a system based on a Monte Carlo method to solve the technical problems of how to improve the complaint processing efficiency and the accuracy of the processing result.
In a first aspect, the invention provides a network complaint prediction method based on a Monte Carlo method, which comprises the following steps:
and (3) data acquisition: collecting network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time;
feature extraction: extracting effective features from the complaint data set, including text features of the complaint and metadata features of the complaint;
feature selection: selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features;
model construction and training: constructing a network complaint prediction model based on a Monte Carlo method, wherein the network complaint prediction model is used for predicting and classifying complaints in a preset time in the future by taking complaint data as input, and performing model training and model evaluation on the network complaint prediction model based on a complaint data set and a feature subset to obtain a trained network complaint prediction model;
prediction and optimization: and predicting and classifying complaints in a preset future time period through the current trained network complaint prediction model by taking the current network complaint data as input, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
Preferably, the metadata features of the complaint include word frequency, keywords, emotional tendency, and complaint time period of the complaint.
Preferably, the following operations are performed during model construction and training:
carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
Preferably, the network complaint prediction model comprises a complaint type prediction model and a complaint channel prediction model;
correspondingly, during model construction and training, training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset through a ware learning method, establishing a complaint type prediction model and a complaint channel prediction model, and adjusting model parameters of the complaint type prediction model and the complaint channel prediction model according to real conditions corresponding to the complaint data set to obtain a trained complaint type prediction model and a trained complaint channel prediction model;
correspondingly, when in prediction and optimization, current network complaint data is taken as input, the complaint type of a future preset time period is predicted through a current trained complaint type prediction model, the current trained complaint classification prediction model is optimized and adjusted according to a prediction result, the complaint channel of the future preset time period is predicted through a current trained complaint channel prediction model, and the current trained complaint channel prediction model is optimized and adjusted according to the prediction result.
In a second aspect, the present invention is a network complaint prediction system based on a monte carlo method, for predicting a network complaint by the network complaint prediction method based on a monte carlo method as described in any one of the first aspects, the system comprising:
the data acquisition module is used for acquiring network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time;
the feature extraction module is used for extracting effective features from the complaint data set, including text features of complaints and metadata features of complaints;
the feature selection module is used for selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features;
the model construction and training module is used for constructing a network complaint prediction model based on a Monte Carlo method, the network complaint prediction model is used for predicting and classifying complaints in a preset time in the future by taking complaint data as input, and model training and model evaluation are carried out on the network complaint prediction model based on a complaint data set and a feature subset to obtain a trained network complaint prediction model;
the prediction and optimization module is used for taking current network complaint data as input, predicting and classifying complaints in a preset time period in the future through the current trained network complaint prediction model, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
Preferably, the metadata features of the complaint include word frequency, keywords, emotional tendency, and complaint time period of the complaint.
Preferably, the model construction and training is used to perform the following operations:
carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
Preferably, the network complaint prediction model comprises a complaint type prediction model and a complaint channel prediction model;
correspondingly, the model construction and training module is used for executing the following steps: training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset through a ware learning method, establishing a complaint type prediction model and a complaint channel prediction model, and adjusting model parameters of the complaint type prediction model and the complaint channel prediction model according to real conditions corresponding to the complaint data set to obtain a trained complaint type prediction model and a trained complaint channel prediction model;
correspondingly, the prediction and optimization module is configured to perform the following: the method comprises the steps of taking current network complaint data as input, predicting the complaint type of a future preset time period through a current trained complaint type prediction model, optimizing and adjusting a current trained complaint classification prediction model according to a prediction result, predicting a complaint channel of the future preset time period through a current trained complaint channel prediction model, and optimizing and adjusting the current trained complaint channel prediction model according to the prediction result.
The network complaint prediction method and system based on the Monte Carlo method have the following advantages:
1. the prediction accuracy is improved: compared with the traditional prediction method, the network complaint prediction method based on the Monte Carlo technology is more accurate, the number and trend of the network complaints can be effectively predicted, and more accurate prediction results can be obtained by analyzing and modeling historical data and simulating various situations possibly occurring in the future, so that the accuracy of operation decisions is improved;
2. the cost is reduced: the application of the technology can help enterprises to identify and process network complaints in time, reduce negative effects and extra cost caused by unprocessed network complaints, avoid resource waste and manpower waste, and improve efficiency and profit;
3. improving user experience: through prediction and quick response to network complaints, user satisfaction and loyalty can be improved to a certain extent, and interaction between the user and the enterprise is further enhanced.
4. Promote the development of industry: the popularization and application of the technology can provide effective guidance and support for the operation and development of the whole industry, and help the industry to realize efficient operation and sustainable development.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of a network complaint prediction method based on the monte carlo method of embodiment 1.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific examples, so that those skilled in the art can better understand the invention and implement it, but the examples are not meant to limit the invention, and the technical features of the embodiments of the invention and the examples can be combined with each other without conflict.
The embodiment of the invention provides a network complaint prediction method and a network complaint prediction system based on a Monte Carlo method, which are used for solving the technical problems of how to improve the complaint processing efficiency and the accuracy of processing results.
Example 1:
the invention discloses a network complaint prediction method based on a Monte Carlo method, which comprises the following steps:
s100, data acquisition: collecting network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time;
s200, feature extraction: extracting effective features from the complaint data set, including text features of the complaint and metadata features of the complaint;
s300, feature selection: selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features;
s400, model construction and training: constructing a network complaint prediction model based on a Monte Carlo method, wherein the network complaint prediction model is used for predicting and classifying complaints in a preset time in the future by taking complaint data as input, and performing model training and model evaluation on the network complaint prediction model based on a complaint data set and a feature subset to obtain a trained network complaint prediction model;
s500, prediction and optimization: and predicting and classifying complaints in a preset future time period through the current trained network complaint prediction model by taking the current network complaint data as input, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
The metadata features of the complaints extracted in step S200 of this embodiment include word frequency, keywords, emotion tendencies, and complaint periods of the complaints.
In the embodiment, step S300 selects the feature subset with the most representativeness and discrimination according to the importance and the relevance of the features, so as to reduce feature dimension and improve the precision and generalization capability of the model.
When the step S400 model is constructed and trained, the following operations are executed:
(1) Carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
(2) Training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
(3) Model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
A Monte Carlo method is adopted to construct a network complaint prediction model, and the method comprises the steps of Monte Carlo sampling, model training, model evaluation and the like. Specifically, monte Carlo sampling is firstly carried out according to the complaint data set, so that a set of complaint data sample sets which accord with the actual situation is obtained. Then, training a network complaint prediction model by adopting a machine learning method, establishing a complaint type and complaint channel prediction model, and adjusting model parameters according to actual conditions. Finally, through evaluating and optimizing the model, the accuracy and generalization capability of the model are improved.
Step S500 predicts and classifies future complaints according to the network complaint prediction model, optimizes and adjusts according to the prediction result and the processing effect, and continuously improves the complaint processing efficiency and the accuracy of the processing result.
Specifically, during model construction and training, a network complaint prediction model is trained through a ware learning method based on a complaint data sample set and a corresponding feature subset, a complaint type prediction model and a complaint channel prediction model are built, model parameters of the complaint type prediction model and the complaint channel prediction model are adjusted according to real conditions corresponding to the complaint data set, and a trained complaint type prediction model and a trained complaint channel prediction model are obtained.
Correspondingly, when in prediction and optimization, current network complaint data is taken as input, the complaint type of a future preset time period is predicted through a current trained complaint type prediction model, the current trained complaint classification prediction model is optimized and adjusted according to a prediction result, the complaint channel of the future preset time period is predicted through a current trained complaint channel prediction model, and the current trained complaint channel prediction model is optimized and adjusted according to the prediction result.
Example 2:
the invention discloses a network complaint prediction system based on a Monte Carlo method, which comprises a data acquisition module, a feature extraction module, a feature selection module, a model construction and training module and a prediction and optimization module, wherein the system can execute the method disclosed in the embodiment 1 to predict the network complaint.
The data acquisition module is used for acquiring network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time.
The feature extraction module is used for extracting effective features from the complaint data set, including text features of complaints and metadata features of complaints.
In this embodiment, the extracted metadata features of the complaints include word frequency, keywords, emotional tendency, and complaint time period of the complaints.
The feature selection module is used for selecting features with representativeness and degree of distinction from the effective features to form feature subsets according to the importance and the relativity of the features.
In this embodiment, the feature selection module is configured to select, according to importance and relevance of features, a feature subset with the most representativeness and distinguishing degree, reduce feature dimensions, and improve accuracy and generalization capability of the model.
The model construction and training module is used for constructing a network complaint prediction model based on a Monte Carlo method, the network complaint prediction model is used for predicting and classifying complaints in a preset future time by taking complaint data as input, and model training and model evaluation are carried out on the network complaint prediction model based on a complaint data set and a feature subset, so that a trained network complaint prediction model is obtained.
The model construction and training module in this embodiment is used for executing the following steps:
(1) Carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
(2) Training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
(3) Model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
A Monte Carlo method is adopted to construct a network complaint prediction model, and the method comprises the steps of Monte Carlo sampling, model training, model evaluation and the like. Specifically, monte Carlo sampling is firstly carried out according to the complaint data set, so that a set of complaint data sample sets which accord with the actual situation is obtained. Then, training a network complaint prediction model by adopting a machine learning method, establishing a complaint type and complaint channel prediction model, and adjusting model parameters according to actual conditions. Finally, through evaluating and optimizing the model, the accuracy and generalization capability of the model are improved.
The prediction and optimization module is used for taking current network complaint data as input, predicting and classifying complaints in a preset time period in the future through the current trained network complaint prediction model, and optimizing and adjusting the current trained network complaint prediction model according to the prediction result.
In this embodiment, the measurement and optimization module is configured to predict and classify future complaints according to the network complaint prediction model, and optimize and adjust the future complaints according to the prediction result and the processing effect, so as to continuously improve the complaint processing efficiency and the accuracy of the processing result.
When the model construction and training is carried out through the model construction and training module, the network complaint prediction model is trained through a ware learning method based on a complaint data sample set and a corresponding feature subset, a complaint type prediction model and a complaint channel prediction model are built, and model parameters of the complaint type prediction model and the complaint channel prediction model are adjusted according to real conditions corresponding to the complaint data set, so that the trained complaint type prediction model and the trained complaint channel prediction model are obtained.
Correspondingly, when predicting and optimizing, the predicting and optimizing are used for executing the following steps: the method comprises the steps of taking current network complaint data as input, predicting the complaint type of a future preset time period through a current trained complaint type prediction model, optimizing and adjusting a current trained complaint classification prediction model according to a prediction result, predicting a complaint channel of the future preset time period through a current trained complaint channel prediction model, and optimizing and adjusting the current trained complaint channel prediction model according to the prediction result.
While the invention has been illustrated and described in detail in the drawings and in the preferred embodiments, the invention is not limited to the disclosed embodiments, and it will be appreciated by those skilled in the art that the code audits of the various embodiments described above may be combined to produce further embodiments of the invention, which are also within the scope of the invention.
Claims (8)
1. The network complaint prediction method based on the Monte Carlo method is characterized by comprising the following steps of:
and (3) data acquisition: collecting network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time;
feature extraction: extracting effective features from the complaint data set, including text features of the complaint and metadata features of the complaint;
feature selection: selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features;
model construction and training: constructing a network complaint prediction model based on a Monte Carlo method, wherein the network complaint prediction model is used for predicting and classifying complaints in a preset time in the future by taking complaint data as input, and performing model training and model evaluation on the network complaint prediction model based on a complaint data set and a feature subset to obtain a trained network complaint prediction model;
prediction and optimization: and predicting and classifying complaints in a preset future time period through the current trained network complaint prediction model by taking the current network complaint data as input, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
2. The method for predicting network complaints based on the monte carlo method according to claim 1, wherein the metadata characteristics of the complaints include word frequency, keywords, emotional tendency, and complaint time period of the complaints.
3. The network complaint prediction method based on the monte carlo method according to claim 1, wherein the following operations are performed during the model construction and training:
carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
4. A network complaint prediction method based on the monte carlo method according to claim 3, wherein the network complaint prediction model includes a complaint type prediction model and a complaint channel prediction model;
correspondingly, during model construction and training, training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset through a ware learning method, establishing a complaint type prediction model and a complaint channel prediction model, and adjusting model parameters of the complaint type prediction model and the complaint channel prediction model according to real conditions corresponding to the complaint data set to obtain a trained complaint type prediction model and a trained complaint channel prediction model;
correspondingly, when in prediction and optimization, current network complaint data is taken as input, the complaint type of a future preset time period is predicted through a current trained complaint type prediction model, the current trained complaint classification prediction model is optimized and adjusted according to a prediction result, the complaint channel of the future preset time period is predicted through a current trained complaint channel prediction model, and the current trained complaint channel prediction model is optimized and adjusted according to the prediction result.
5. A network complaint prediction system based on the monte carlo method for predicting a network complaint by the network complaint prediction method based on the monte carlo method as claimed in any one of claims 1 to 4, the system comprising:
the data acquisition module is used for acquiring network complaint data to construct a complaint data set, wherein the network complaint data comprises complaint types, complaint channels and complaint time;
the feature extraction module is used for extracting effective features from the complaint data set, including text features of complaints and metadata features of complaints;
the feature selection module is used for selecting features with representativeness and distinguishing degree from the effective features to form feature subsets according to the importance and the relativity of the features;
the model construction and training module is used for constructing a network complaint prediction model based on a Monte Carlo method, the network complaint prediction model is used for predicting and classifying complaints in a preset time in the future by taking complaint data as input, and model training and model evaluation are carried out on the network complaint prediction model based on a complaint data set and a feature subset to obtain a trained network complaint prediction model;
the prediction and optimization module is used for taking current network complaint data as input, predicting and classifying complaints in a preset time period in the future through the current trained network complaint prediction model, and optimizing and adjusting the current trained network complaint prediction model according to a prediction result.
6. The network complaint prediction system based on the monte carlo method according to claim 5, wherein the metadata characteristics of the complaint include word frequency, keyword, emotional tendency, and complaint period of the complaint.
7. The network complaint prediction system based on the monte carlo method according to claim 5, wherein the model construction and training is used to perform the following operations:
carrying out Monte Carlo sampling according to the complaint data set to obtain a set of complaint data sample sets conforming to the real situation;
training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset by a ware learning method, and adjusting model parameters according to the real situation corresponding to the complaint data set to obtain a trained network prediction model;
model evaluation is performed on the trained network prediction model based on the complaint data sample set and the corresponding feature subset.
8. The network complaint prediction system based on the monte carlo method according to claim 7, wherein the network complaint prediction model includes a complaint type prediction model and a complaint channel prediction model;
correspondingly, the model construction and training module is used for executing the following steps: training a network complaint prediction model based on a complaint data sample set and a corresponding feature subset through a ware learning method, establishing a complaint type prediction model and a complaint channel prediction model, and adjusting model parameters of the complaint type prediction model and the complaint channel prediction model according to real conditions corresponding to the complaint data set to obtain a trained complaint type prediction model and a trained complaint channel prediction model;
correspondingly, the prediction and optimization module is configured to perform the following: the method comprises the steps of taking current network complaint data as input, predicting the complaint type of a future preset time period through a current trained complaint type prediction model, optimizing and adjusting a current trained complaint classification prediction model according to a prediction result, predicting a complaint channel of the future preset time period through a current trained complaint channel prediction model, and optimizing and adjusting the current trained complaint channel prediction model according to the prediction result.
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