CN112036173A - Method and system for processing telemarketing text - Google Patents
Method and system for processing telemarketing text Download PDFInfo
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Abstract
The invention discloses a method and a system for processing an e-mail text, wherein the method comprises the following steps: acquiring a current call text of a target user; acquiring a pre-constructed neural network model; and inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user. The method and the device can identify the current call text of the target user through the neural network model, effectively analyze the user characteristics in the telemarketing content communicated with the user, and further more accurately analyze the user intention.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for processing an e-commerce text.
Background
At present, in the field of electric marketing, whether historical users or new users, the purchasing will of the users needs to be analyzed frequently so as to carry out targeted sales on people with high willingness. The method widely used at present mainly aims at analyzing the attributes and behavior characteristics of users, and achieves certain effects, but the bottleneck is easy to achieve. While the content of the call with the user is not well utilized during the telemarketing process.
Therefore, how to effectively analyze the user characteristics from the electricity sales content communicated with the user is an urgent problem to be solved.
Disclosure of Invention
In view of this, the present invention provides a method for processing an e-commerce text, which can effectively analyze user characteristics from e-commerce contents communicated with a user, and further can more accurately analyze a user intention.
The invention provides a processing method of an e-mail text, which comprises the following steps:
acquiring a current call text of a target user;
acquiring a pre-constructed neural network model;
and inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
Preferably, constructing the neural network model comprises:
acquiring a historical call text;
preprocessing the historical call text to obtain a processed call text;
performing word segmentation processing on the processed call text to obtain a word segmentation result;
training a word vector model based on the word segmentation result to obtain a trained word vector model;
converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
determining the attention points and named entities of the users under a specific service scene based on the clustering result;
obtaining a user text and a corresponding named entity of a determined category based on the clustering result, the attention point and the named entity;
and based on the user text of the determined category and the corresponding named entity, utilizing a deep neural network training model to obtain a neural network model.
Preferably, the preprocessing the historical call text to obtain a processed call text includes:
and removing the salesman call text and stop words in the historical call text to obtain a processed call text.
A system for processing e-mail text, comprising:
the first acquisition module is used for acquiring the current call text of a target user;
the second acquisition module is used for acquiring a pre-constructed neural network model;
and the recognition module is used for inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
Preferably, the system further comprises: a building module for building a neural network model, wherein the building module comprises:
the acquisition unit is used for acquiring a historical call text;
the preprocessing unit is used for preprocessing the historical call text to obtain a processed call text;
the word segmentation unit is used for carrying out word segmentation processing on the processed call text to obtain a word segmentation result;
the first training unit is used for training the word vector model based on the word segmentation result to obtain a trained word vector model;
the clustering unit is used for converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
the determining unit is used for determining the attention points and the named entities of the users under the specific service scene based on the clustering result;
the processing unit is used for obtaining a user text of a determined category and a corresponding named entity based on the clustering result, the attention point and the named entity;
and the second training unit is used for training a model by utilizing a deep neural network based on the user texts of the determined categories and the corresponding named entities to obtain a neural network model.
Preferably, the preprocessing unit is specifically configured to:
and removing the salesman call text and stop words in the historical call text to obtain a processed call text.
An electronic device, comprising: at least one processor, and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to call program instructions in the memory to perform the method for processing e-mail text as described above.
A storage medium having stored therein computer-executable instructions that, when loaded and executed by a processor, implement a method of processing e-mail text as described above.
In summary, the present invention discloses a processing method of an e-commerce text, when the e-commerce text needs to be processed, first obtaining a current call text of a target user, and simultaneously obtaining a pre-constructed neural network model; and then, inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user. The method and the device can identify the current call text of the target user through the neural network model, and effectively analyze the user characteristics in the telemarketing content communicated with the user.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment 1 of a method for processing an e-mail text disclosed by the present invention;
FIG. 2 is a flowchart of an embodiment 2 of a method for processing an e-mail text according to the present disclosure;
FIG. 3 is a schematic structural diagram of an embodiment 1 of a system for processing an e-mail document according to the present disclosure;
FIG. 4 is a schematic structural diagram of an embodiment 2 of the processing system for the e-mail text disclosed in the present invention;
fig. 5 is a schematic structural diagram of an electronic device disclosed in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, which is a flowchart of embodiment 1 of a method for processing an e-mail text disclosed in the present invention, the method may include the following steps:
s101, acquiring a current call text of a target user;
when the e-mail text needs to be processed, the current call text of the target user is firstly acquired, namely the current call text of the user needing text processing is firstly acquired. When the current call text of the target user is obtained, the current call voice of the target user can be subjected to voice recognition, and the current call text of the target user is obtained.
S102, acquiring a pre-constructed neural network model;
meanwhile, a pre-constructed neural network model is obtained.
S103, inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
And then, inputting the acquired current call text of the target user into a neural network model, and identifying the current call text of the target user through the neural network model to obtain the text characteristics of the target user.
In summary, in the above embodiment, when the e-commerce text needs to be processed, the current call text of the target user is obtained first, and a pre-constructed neural network model is obtained at the same time; and then, inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user. The current call text of the target user can be identified through the neural network model, and the user characteristics in the electricity marketing content communicated with the user are effectively analyzed.
As shown in fig. 2, which is a flowchart of embodiment 2 of a method for processing an e-mail text disclosed in the present invention, the method may include the following steps:
s201, acquiring a historical call text;
when the e-sale text needs to be processed, a neural network model for recognizing the e-sale text is firstly constructed.
When a neural network model is constructed, a historical call text in a historical electricity selling process is obtained at first. When the historical call text is obtained, the historical call voice in the electricity selling process can be subjected to voice recognition, and the historical call text is obtained.
S202, preprocessing the historical call text to obtain a processed call text;
after the historical call text is obtained, the obtained historical call text is further preprocessed, and a processed call text is obtained.
Specifically, when the historical call text is preprocessed, the call text and stop words of salesmen in the historical call text can be removed, the interference of the call text of the salesmen can be abandoned, and only the user text is analyzed subsequently.
S203, performing word segmentation processing on the processed call text to obtain a word segmentation result;
and then, performing word segmentation processing on the obtained processed call text to obtain a word segmentation result of the processed call text.
S204, training the word vector model based on the word segmentation result to obtain a trained word vector model;
and training the word vector model according to the obtained word segmentation result of the processed call text to obtain the trained word vector model.
S205, converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
and then, according to the obtained trained word vector model, converting the words into a vector form for clustering to obtain a clustering result.
S206, determining the attention points and named entities of the users in the specific service scene based on the clustering result;
and then according to the obtained clustering result, determining the attention point and the named entity of the user in a specific service scene.
S207, obtaining a user text of a determined category and a corresponding named entity based on the clustering result, the focus and the named entity;
and then, according to the obtained clustering result and the determined attention points and named entities of the user in the specific service scene, further obtaining the user text of the determined category and the corresponding named entities.
S208, based on the user texts with the determined categories and the corresponding named entities, utilizing a deep neural network training model to obtain a neural network model;
and according to the obtained user text of the determined category and the corresponding named entity, utilizing a neural network training model to obtain a neural network model.
S209, acquiring a current call text of a target user;
the method comprises the steps of obtaining a current call text of a target user, namely obtaining the current call text of a user needing text processing. When the current call text of the target user is obtained, the current call voice of the target user can be subjected to voice recognition, and the current call text of the target user is obtained.
S210, acquiring a pre-constructed neural network model;
meanwhile, a pre-constructed neural network model is obtained.
S211, inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
And then, inputting the acquired current call text of the target user into a neural network model, and identifying the current call text of the target user through the neural network model to obtain the text characteristics of the target user.
In summary, on the basis of the above embodiments, when the neural network model is constructed, the historical call text may be specifically obtained; preprocessing the historical call text to obtain a processed call text; performing word segmentation on the processed call text to obtain a word segmentation result; training the word vector model based on the word segmentation result to obtain a trained word vector model; converting words into a vector form based on the trained word vector model for clustering to obtain a clustering result; determining the attention points and named entities of the users under the specific service scene based on the clustering result; obtaining a user text and a corresponding named entity of a determined category based on the clustering result, the attention point and the named entity; and based on the user texts with the determined categories and the corresponding named entities, utilizing a deep neural network training model to obtain a neural network model.
As shown in fig. 3, which is a schematic structural diagram of an embodiment 1 of the system for processing an e-mail text disclosed in the present invention, the system may include:
a first obtaining module 31, configured to obtain a current call text of a target user;
a second obtaining module 32, configured to obtain a pre-constructed neural network model;
and the identification module 33 is configured to input the current call text of the target user into the neural network model for identification, so as to obtain a text feature of the target user.
The working principle of the processing system of the e-mail text disclosed in this embodiment is the same as that of embodiment 1 of the processing method of the e-mail text described above, and is not described herein again.
As shown in fig. 4, which is a schematic structural diagram of an embodiment 2 of the system for processing an e-mail text disclosed in the present invention, the system may include:
an obtaining unit 411 in the building module 41, configured to obtain a historical call text;
the preprocessing unit 412 in the building module 41 is configured to preprocess the historical call text to obtain a processed call text;
a word segmentation unit 413 in the construction module 41, configured to perform word segmentation on the processed call text to obtain a word segmentation result;
a first training unit 414 in the building module 41, configured to train the word vector model based on the word segmentation result, so as to obtain a trained word vector model;
a clustering unit 415 in the building module 41, configured to convert words into vector forms for clustering based on the trained word vector model, so as to obtain a clustering result;
a determining unit 416 in the building module 41, configured to determine, based on the clustering result, a point of interest and a named entity of the user in the specific service scenario;
a processing unit 417 in the building module 41, configured to obtain a user text of a certain category and a corresponding named entity based on the clustering result, the focus and the named entity;
a second training unit 418 in the building module 41, configured to train a model by using a deep neural network based on the user text of the determined category and the corresponding named entity, to obtain a neural network model;
a first obtaining module 42, configured to obtain a current call text of a target user;
a second obtaining module 43, configured to obtain a pre-constructed neural network model;
and the identification module 44 is configured to input the current call text of the target user into the neural network model for identification, so as to obtain a text feature of the target user.
The working principle of the processing system of the e-mail text disclosed in this embodiment is the same as that of the processing method of the e-mail text embodiment 2, and is not described herein again.
As shown in fig. 5, an embodiment of the present invention provides an electronic device 50, where the electronic device 50 includes at least one processor 501, at least one memory 502 connected to the processor 501, and a bus 503; the processor 501 and the memory 502 complete communication with each other through the bus 503; the processor 501 is used for calling the program instructions in the memory 502 to execute the above-mentioned processing method of the e-mail text. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The invention also provides a storage medium adapted to perform a program for initializing the following method steps when executed on a data processing device:
acquiring a current call text of a target user;
acquiring a pre-constructed neural network model;
and inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
Optionally, constructing the neural network model comprises:
acquiring a historical call text;
preprocessing the historical call text to obtain a processed call text;
performing word segmentation processing on the processed call text to obtain a word segmentation result;
training a word vector model based on the word segmentation result to obtain a trained word vector model;
converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
determining the attention points and named entities of the users under a specific service scene based on the clustering result;
obtaining a user text and a corresponding named entity of a determined category based on the clustering result, the attention point and the named entity;
and based on the user text of the determined category and the corresponding named entity, utilizing a deep neural network training model to obtain a neural network model.
Optionally, the preprocessing the historical call text to obtain a processed call text includes:
and removing the salesman call text and stop words in the historical call text to obtain a processed call text.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (6)
1. A method for processing a telemarketing text, comprising:
acquiring a historical call text;
preprocessing the historical call text to obtain a processed call text;
performing word segmentation processing on the processed call text to obtain a word segmentation result;
training a word vector model based on the word segmentation result to obtain a trained word vector model;
converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
determining the attention points and named entities of the users under a specific service scene based on the clustering result;
obtaining a user text and a corresponding named entity of a determined category based on the clustering result, the attention point and the named entity;
based on the user texts with the determined categories and the corresponding named entities, utilizing a deep neural network training model to obtain a neural network model;
acquiring a current call text of a target user;
acquiring a pre-constructed neural network model;
and inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
2. The method of claim 1, wherein the pre-processing the historical call text to obtain a processed call text comprises:
and removing the salesman call text and stop words in the historical call text to obtain a processed call text.
3. A system for processing e-mail text, comprising:
a building module for building a neural network model, wherein the building module comprises:
the acquisition unit is used for acquiring a historical call text;
the preprocessing unit is used for preprocessing the historical call text to obtain a processed call text;
the word segmentation unit is used for carrying out word segmentation processing on the processed call text to obtain a word segmentation result;
the first training unit is used for training the word vector model based on the word segmentation result to obtain a trained word vector model;
the clustering unit is used for converting words into a vector form for clustering based on the trained word vector model to obtain a clustering result;
the determining unit is used for determining the attention points and the named entities of the users under the specific service scene based on the clustering result;
the processing unit is used for obtaining a user text of a determined category and a corresponding named entity based on the clustering result, the attention point and the named entity;
the second training unit is used for utilizing a deep neural network training model to obtain a neural network model based on the user text of the determined category and the corresponding named entity;
the first acquisition module is used for acquiring the current call text of a target user;
the second acquisition module is used for acquiring a pre-constructed neural network model;
and the recognition module is used for inputting the current call text of the target user into the neural network model for recognition to obtain the text characteristics of the target user.
4. The system of claim 3, wherein the preprocessing unit is specifically configured to:
and removing the salesman call text and stop words in the historical call text to obtain a processed call text.
5. An electronic device, comprising: at least one processor, and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the method of processing e-mail text according to claim 1 or 2.
6. A storage medium having stored thereon computer-executable instructions which, when loaded and executed by a processor, carry out a method of processing e-mail text according to claim 1 or 2.
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Application publication date: 20201204 |