CN107770738B - Method and user terminal for realizing automatic short message classification - Google Patents
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- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/7243—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages
- H04M1/72436—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages for text messaging, e.g. short messaging services [SMS] or e-mails
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Abstract
The invention discloses a method and a user terminal for realizing automatic short message classification, and relates to the field of big data. After receiving the short message, the user terminal judges whether the short message is matched with the number rule of the sender, if the short message is matched with the number rule of the sender, the short message is stored in a corresponding classification folder, if the short message is not matched with the number rule of the sender, the user terminal further judges whether the short message is matched with a text classifier, and if the short message is matched with the text classifier, the user terminal stores the short message in the corresponding classification folder. The invention can realize the short message classification without inputting keywords and the number of the sender by a user, overcomes the defects of variable numbers of the sender and difficult manual keyword determination, and can more intelligently solve the problem of automatic short message classification.
Description
Technical Field
The invention relates to the field of big data, in particular to a method and a user terminal for realizing automatic short message classification.
Background
The mobile phone short message contains a large amount of notification information, such as e-commerce promotion, bank financing, family-school connection, weather forecast, and the like, and the user has a significant demand for classified management of the short message.
Fig. 1 shows a method for classifying mobile phone short messages in the prior art, which realizes automatic classification of short messages by matching with a sender number and a keyword text which are preset by a user. Wherein:
The setting can be performed by directly inputting a number, selecting a number from an address book, selecting a number from a call record, and the like.
And step 102b, setting short message content matching conditions.
Wherein a condition for the input text to completely match or the input text to partially match may be set.
And step 103, receiving a new short message after the setting is finished.
And 104, judging whether the new short message is matched with the set condition. If so, go to step 105, otherwise go to step 106.
And 105, storing the new short message into the matched classified folder.
And step 106, storing the new short message into an inbox.
However, the prior art has the following problems: the merchant may use a plurality of port numbers to send short messages; it is difficult to manually determine keywords for each category, for example, many short messages for weather forecast category do not actually contain the text of "weather".
Disclosure of Invention
The embodiment of the invention provides a method and a user terminal for realizing automatic short message classification, which can realize short message classification without inputting keywords and sender numbers by a user, overcome the defects of variable sender numbers and difficulty in manually determining the keywords, and further more intelligently solve the problem of automatic short message classification.
According to an aspect of the present invention, a method for implementing automatic classification of short messages is provided, which includes:
after the user terminal receives the short message, judging whether the short message is matched with a sender number rule;
if the short message is matched with the number rule of the sender, storing the short message into a corresponding classification folder;
if the short message is not matched with the number rule of the sender, further judging whether the short message is matched with the text classifier;
and if the short message is matched with the text classifier, storing the short message into a corresponding classification folder.
In one embodiment, after storing the short message into the corresponding classification folder, the method further includes:
the classification center vector of the corresponding classification folder is recalculated to update the text classifier.
In one embodiment, if the short message does not match the text classifier, the short message is stored in a short message inbox.
In one embodiment, the step of determining whether the short message matches the text classifier comprises:
determining a text vector of the short message;
respectively calculating the similarity between the text vector of the short message and each classification center vector in the text classifier;
and under the condition that at least one similarity exceeds a preset threshold value, judging that the short message is matched with the text classifier, and taking the classification folder corresponding to the maximum similarity as the classification folder corresponding to the short message.
In one embodiment, the method further comprises the steps of learning sender number rules and constructing a text classifier, wherein:
creating a classification folder;
adding the received short messages into the created classified folder;
learning sender number rules so as to add sender number rules corresponding to the sender number of the received short message and the classification folder according to user requirements, and adding all short messages sent by the sender number of the received short message into the classification folder;
the classification center vectors in the classification folder are computed to construct a text classifier.
In one embodiment, learning sender number rules comprises:
judging whether the number of a sender of the received short message is included in the number rule of the sender;
if the sender number of the received short message is not included in the sender number rule, further inquiring whether the user requires to bind the sender number of the received short message with the classification folder;
if the user requires to bind the sender number of the received short message with the classification folder, adding a sender number rule corresponding to the sender number of the received short message and the classification folder, and adding all short messages sent by the sender number of the received short message into the classification folder.
In one embodiment, if the sender number of the received short message is included in the sender number rule, further determining whether the sender number of the received short message conflicts with the sender number rule;
and if the sender number of the received short message conflicts with the sender number rule, deleting the sender number rule.
In one embodiment, the step of calculating a classification center vector in the classification folder to construct a text classifier comprises:
calculating text vectors of all short messages in the classification folder;
and taking the average value of the text vectors of the short messages in the classification folder as a classification center vector of the classification folder to construct a text classifier.
According to another aspect of the present invention, there is provided a user terminal for implementing automatic classification of short messages, including an interface module, a number rule module, a text classification module and a classification management module, wherein:
the interface module is used for receiving the short message;
the number rule module is used for judging whether the short message is matched with the number rule of the sender or not after the interface module receives the short message;
the text classification module is used for judging whether the short message is matched with the text classifier or not under the condition that the short message is not matched with the number rule of the sender;
and the classification management module is used for storing the short message into a corresponding classification folder under the condition that the number rule module judges that the short message is matched with the number rule of the sender or under the condition that the text classification module judges that the short message is matched with the text classifier.
In one embodiment, the user terminal further includes an update module, wherein:
and the updating module is used for recalculating the classification center vector of the corresponding classification folder after the classification management module stores the short message into the corresponding classification folder so as to update the text classifier.
In one embodiment, the classification management module is further configured to store the short message into the short message inbox if the short message does not match the text classifier.
In one embodiment, the text classification module determines a text vector of the short message when determining whether the short message matches the text classifier, respectively calculates the similarity between the text vector of the short message and each classification center vector in the text classifier, determines that the short message matches the text classifier when at least one similarity exceeds a predetermined threshold, and takes a classification folder corresponding to the maximum similarity as a classification folder corresponding to the short message.
In one embodiment, the user terminal further includes a classification labeling module, where:
the classification labeling module is used for creating a classification folder and adding the received short messages into the created classification folder; learning sender number rules so as to add sender number rules corresponding to the sender number of the received short message and the classification folder according to user requirements, and adding all short messages sent by the sender number of the received short message into the classification folder; the classification center vectors in the classification folder are computed to construct a text classifier.
In one embodiment, the classification labeling module specifically judges whether the sender number of the received short message is included in the sender number rule when learning the sender number rule, and further inquires whether the user requires to bind the sender number of the received short message with the classification folder if the sender number of the received short message is not included in the sender number rule; if the user requires to bind the sender number of the received short message with the classification folder, adding a sender number rule corresponding to the sender number of the received short message and the classification folder, and adding all short messages sent by the sender number of the received short message into the classification folder.
In one embodiment, the classification labeling module is further configured to further determine whether the sender number of the received short message conflicts with the sender number rule when the sender number of the received short message is included in the sender number rule; and if the sender number of the received short message conflicts with the sender number rule, deleting the sender number rule.
In one embodiment, the classification labeling module calculates the text vector of each short message in the classification folder when constructing the text classifier, and takes the average value of the text vectors of each short message in the classification folder as the classification center vector of the classification folder to construct the text classifier.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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, and 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 these drawings without creative efforts.
Fig. 1 is a schematic diagram of a short message classification process in the prior art.
Fig. 2 is a schematic diagram of an embodiment of a method for implementing automatic short message classification according to the present invention.
FIG. 3 is a diagram illustrating an embodiment of determining whether a short message matches a text classifier according to the present invention.
FIG. 4 is a diagram illustrating a process of labeling according to one embodiment of the present invention.
Fig. 5 is a diagram illustrating an embodiment of learning sender number rules according to the present invention.
FIG. 6 is a diagram of one embodiment of constructing a text classifier according to the present invention.
Fig. 7 is a schematic diagram of an embodiment of a user terminal for implementing automatic short message classification according to the present invention.
Fig. 8 is a schematic diagram of another embodiment of a user terminal for implementing automatic short message classification according to 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. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. 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.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 2 is a schematic diagram of an embodiment of a method for implementing automatic short message classification according to the present invention. Wherein:
If the sender number rule comprises the short message, the short message can be judged to be matched with the sender number rule.
And step 204, storing the short messages into corresponding classification folders.
Optionally, after step 204, step 206 may also be performed.
Based on the method for realizing the automatic classification of the short messages provided by the embodiment of the invention, the classification of the short messages to be classified is determined based on the number rule of the sender and the text classifier, the short messages can be classified without inputting keywords and the number of the sender by a user, the defects of variable numbers of the sender and difficulty in manually determining the keywords are overcome, and the problem of automatic classification of the short messages can be solved more intelligently.
Alternatively, the step of determining whether the short message matches the text classifier may be as shown in fig. 3. Wherein:
The vectorization representation of the short message text to be classified can include the following processing: text normalization processing (capital and small-form, full-angle and half-angle conversion) → denoising (removing stop words and imaginary words) → participle → word frequency statistics → text vector representation.
For example, the similarity between the short message vector to be classified and each classification center vector can be sequentially calculated by using a vector space cosine similarity formula.
And step 304, judging that the short message is matched with the text classifier, and taking the classification folder corresponding to the maximum similarity as the classification folder corresponding to the short message.
Fig. 4 is a schematic diagram of an embodiment of a classification labeling process of the present invention, by which learning sender number rules and constructing a text classifier can be realized. Wherein:
The following describes the learning of sender number rules and the construction of text classifiers.
Fig. 5 is a diagram illustrating an embodiment of learning sender number rules according to the present invention. Wherein:
Through the process, the number rule of the sender can be automatically learned through the received short message.
FIG. 6 is a diagram of one embodiment of constructing a text classifier according to the present invention. Wherein:
Wherein, each short message text under the classification can be vectorized in turn: text normalization processing (capital and small-form, full-angle and half-angle conversion) → denoising (removing stop word and imaginary word) → word segmentation → word frequency statistics → text vector representation.
For example, the classification center vector is calculated as the arithmetic mean of the text vectors of the short messages in the category.
Through the process, the text classifier can be automatically constructed through the received short messages.
Fig. 7 is a schematic diagram of an embodiment of a user terminal for implementing automatic short message classification according to the present invention. As shown in fig. 7, the user terminal may include an interface module 701, a number rule module 702, a text classification module 703, and a classification management module 704. Wherein:
the interface module 701 is used for receiving a short message.
The number rule module 702 is configured to determine whether the short message matches the sender number rule after the interface module 701 receives the short message.
The text classification module 703 is configured to determine whether the short message matches the text classifier under the condition that the short message does not match the sender number rule.
The classification management module 704 is configured to store the short message into a corresponding classification folder when the number rule module 702 determines that the short message matches the number rule of the sender, or when the text classification module 703 determines that the short message matches the text classifier.
Optionally, the classification management module 704 is further configured to store the short message into a short message inbox if the short message does not match the text classifier.
Optionally, the text classification module 704 further determines a text vector of the short message when determining whether the short message matches the text classifier, respectively calculates similarities between the text vector of the short message and each classification center vector in the text classifier, determines that the short message matches the text classifier when at least one of the similarities exceeds a predetermined threshold, and takes a classification folder corresponding to the largest similarity as a classification folder corresponding to the short message.
Based on the user terminal for realizing the automatic classification of the short messages provided by the embodiment of the invention, the classification of the short messages to be classified is determined based on the number rule of the sender and the text classifier, the short messages can be classified without inputting keywords and the number of the sender by a user, the defects of variable numbers of the sender and difficulty in manually determining the keywords are overcome, and the problem of automatic classification of the short messages can be solved more intelligently.
Fig. 8 is a schematic diagram of another embodiment of a user terminal for implementing automatic short message classification according to the present invention. In contrast to the embodiment shown in fig. 7, in the embodiment shown in fig. 8, an update module 805 is included in addition to the interface module 801, the number rule module 802, the text classification module 803, and the classification management module 804. Wherein:
the updating module 805 is configured to recalculate the classification center vector of the corresponding classification folder after the classification management module 804 stores the short message into the corresponding classification folder, so as to update the text classifier.
Optionally, as shown in fig. 8, the user terminal further includes a category labeling module 806. Wherein:
the classification labeling module 806 is configured to create a classification folder, and add the received short message to the created classification folder; learning sender number rules so as to add sender number rules corresponding to the sender number of the received short message and the classification folder according to user requirements, and adding all short messages sent by the sender number of the received short message into the classification folder; the classification center vectors in the classification folder are computed to construct a text classifier.
Optionally, the classification labeling module 806 specifically determines whether the sender number of the received short message is included in the sender number rule when learning the sender number rule, and further queries whether the user requires to bind the sender number of the received short message with the classification folder if the sender number of the received short message is not included in the sender number rule; if the user requires to bind the sender number of the received short message with the classification folder, adding a sender number rule corresponding to the sender number of the received short message and the classification folder, and adding all short messages sent by the sender number of the received short message into the classification folder.
In addition, in the case that the sender number of the received short message is included in the sender number rule, the classification labeling module 806 further determines whether the sender number of the received short message conflicts with the sender number rule; and if the sender number of the received short message conflicts with the sender number rule, deleting the sender number rule.
Optionally, the classification labeling module 806 is further configured to, when constructing the text classifier, calculate a text vector of each short message in the classification folder, and use an average value of the text vectors of each short message in the classification folder as a classification center vector of the classification folder, so as to construct the text classifier.
By implementing the invention, the following beneficial effects can be obtained:
1) when the user classifies and arranges the existing short messages, a sender number rule is established by pulling the short messages in the classified folder, and the user does not need to input or select from an address list;
2) by pulling in the short messages in the classification folder, content classification rules are automatically learned, and a user does not need to define classified keyword texts;
3) the defects that the number of the sender is variable and the keywords are difficult to determine manually are overcome, so that the problem of automatic short message classification can be solved more intelligently.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (8)
1. A method for realizing automatic classification of short messages is characterized by comprising the following steps:
learning a sender number rule and constructing a text classifier;
after a user terminal receives a short message, judging whether the short message is matched with a sender number rule or not;
if the short message is matched with the number rule of the sender, storing the short message into a corresponding classification folder;
if the short message is not matched with the sender number rule, further judging whether the short message is matched with a text classifier;
if the short message is matched with the text classifier, storing the short message into a corresponding classification folder;
recalculating a classification center vector of the corresponding classification folder so as to update a text classifier;
the step of judging whether the short message is matched with the text classifier comprises the following steps:
determining a text vector of the short message;
respectively calculating the similarity between the text vector of the short message and each classification center vector in a text classifier;
under the condition that at least one similarity exceeds a preset threshold value, judging that the short message is matched with a text classifier, and taking a classification folder corresponding to the maximum similarity as a classification folder corresponding to the short message;
wherein, studying sender number rules and constructing a text classifier comprises:
creating a classification folder;
adding the received short messages into the created classified folder;
learning a sender number rule, wherein whether a sender number of a received short message is included in the sender number rule is judged, if the sender number of the received short message is not included in the sender number rule, whether a user requires to bind the sender number of the received short message with the classification folder is further inquired, if the user requires to bind the sender number of the received short message with the classification folder, a sender number rule corresponding to the sender number of the received short message and the classification folder is added, and all short messages sent by the sender number of the received short message are added into the classification folder;
the classification center vectors in the classification folder are computed to construct a text classifier.
2. The method of claim 1,
and if the short message is not matched with the text classifier, storing the short message into a short message inbox.
3. The method according to claim 1 or 2,
if the sender number of the received short message is included in the sender number rule, further judging whether the sender number of the received short message conflicts with the sender number rule;
and if the sender number of the received short message conflicts with the sender number rule, deleting the sender number rule.
4. The method according to claim 1 or 2,
the step of calculating the classification center vector in the classification folder to construct a text classifier comprises:
calculating text vectors of all short messages in the classification folder;
and taking the average value of the text vectors of the short messages in the classification folder as a classification center vector of the classification folder to construct a text classifier.
5. The utility model provides a user terminal for realizing SMS automatic classification which characterized in that, includes categorised mark module, interface module, number rule module, text classification module, categorised management module and update module, wherein:
the classification labeling module is used for creating a classification folder and adding the received short messages into the created classification folder; learning a sender number rule, wherein whether a sender number of a received short message is included in the sender number rule is judged, if the sender number of the received short message is not included in the sender number rule, whether a user requires to bind the sender number of the received short message with the classification folder is further inquired, if the user requires to bind the sender number of the received short message with the classification folder, a sender number rule corresponding to the sender number of the received short message and the classification folder is added, and all short messages sent by the sender number of the received short message are added into the classification folder; calculating a classification center vector in the classification folder to construct a text classifier;
the interface module is used for receiving the short message;
the number rule module is used for judging whether the short message is matched with the number rule of the sender or not after the interface module receives the short message;
the text classification module is used for judging whether the short message is matched with a text classifier or not under the condition that the short message is not matched with the number rule of the sender, wherein when the short message is judged to be matched with the text classifier or not, the text vector of the short message is determined, the similarity between the text vector of the short message and each classification center vector in the text classifier is respectively calculated, and under the condition that at least one similarity exceeds a preset threshold value, the short message is judged to be matched with the text classifier, and a classification folder corresponding to the maximum similarity is used as a classification folder corresponding to the short message;
the classification management module is used for storing the short message into a corresponding classification folder under the condition that the number rule module judges that the short message is matched with the number rule of the sender or under the condition that the text classification module judges that the short message is matched with the text classifier;
and the updating module is used for recalculating the classification center vector of the corresponding classification folder after the classification management module stores the short message into the corresponding classification folder so as to update the text classifier.
6. The user terminal of claim 5,
the classification management module is also used for storing the short message into a short message inbox under the condition that the short message is not matched with the text classifier.
7. The user terminal according to claim 5 or 6,
the classification marking module is also used for further judging whether the sender number of the received short message conflicts with the sender number rule under the condition that the sender number of the received short message is included in the sender number rule; and if the sender number of the received short message conflicts with the sender number rule, deleting the sender number rule.
8. The user terminal according to claim 5 or 6,
and when constructing the text classifier, the classification labeling module calculates the text vectors of the short messages in the classification folder, and takes the average value of the text vectors of the short messages in the classification folder as the classification center vector of the classification folder so as to construct the text classifier.
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