CN113177170A - Comment display method and device and electronic equipment - Google Patents

Comment display method and device and electronic equipment Download PDF

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CN113177170A
CN113177170A CN202110393038.XA CN202110393038A CN113177170A CN 113177170 A CN113177170 A CN 113177170A CN 202110393038 A CN202110393038 A CN 202110393038A CN 113177170 A CN113177170 A CN 113177170A
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content
score
user
comment
target user
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CN113177170B (en
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汤丽
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Vivo Mobile Communication Co Ltd
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Vivo Mobile Communication Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The application discloses a comment display method and device and electronic equipment, and belongs to the technical field of computers. The method comprises the following steps: acquiring first content displayed by a display interface; determining a target user characteristic matched with the first content from a target user characteristic set based on the first content; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic; and displaying the comment corresponding to the first content according to the first content and the characteristics of the target user.

Description

Comment display method and device and electronic equipment
Technical Field
The application belongs to the technical field of computers, and particularly relates to a comment display method and device and electronic equipment.
Background
User comments are involved in various scenes of product purchase, information browsing and the like, and people often obtain more products, information and the like which are closer to the requirements of the users from comments issued by other users. In the prior art, when the comments of the user are displayed, the comments are displayed after being sorted according to the posting time or the number of praise and the like of the comments, however, the personalized requirements of the user cannot be met, so that the user needs to spend a lot of time on browsing the displayed comments to find the comments meeting the requirements of the user.
Disclosure of Invention
The embodiment of the application aims to provide a comment displaying method, which can solve the problem that comments cannot be displayed individually for different users.
In a first aspect, an embodiment of the present application provides a comment displaying method, where the method includes:
acquiring first content displayed by a display interface;
determining a target user characteristic matched with the first content from a target user characteristic set based on the first content; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic;
and displaying the comment corresponding to the first content according to the first content and the characteristics of the target user.
In a second aspect, an embodiment of the present application provides a comment displaying apparatus, where the apparatus includes:
the acquisition module is used for acquiring first content displayed by a display interface;
a determining module, configured to determine, based on the first content, a target user feature that matches the first content from a target user feature set; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic;
and the display module is used for displaying the comments corresponding to the first content according to the first content and the characteristics of the target user.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instructions stored on the memory and executable on the processor, and when executed by the processor, the program or instructions implement the steps of the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, which when executed by a processor implement the steps of the method according to the first aspect.
In a fifth aspect, an embodiment of the present application provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or instructions to implement the method according to the first aspect.
In the embodiment of the application, after first content displayed on a display interface is acquired, a target user characteristic matched with the first content is determined from a target user characteristic set based on the first content, and a comment corresponding to the first content is displayed according to the first content and the matched target user characteristic. The target user characteristics comprise the first user characteristics and the second user characteristics, and the second user characteristics are sub-characteristics under the first user characteristics, so that the second user characteristics can better reflect the favor of the target user to the comments, namely, the comments can be distinguished according to the favor of the user in a finer granularity mode, the comments concerned by the user and the interested comments can be arranged in front, and personalized comment display is realized.
Drawings
FIG. 1 is a flow chart of a comment displaying method provided by an embodiment of the present application;
2-5 are interface display diagrams of electronic devices provided by embodiments of the present application;
FIG. 6 is a flow chart of a review presentation method of an example of the present application;
FIG. 7 is a schematic structural diagram of a comment displaying apparatus provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present application are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that embodiments of the application may be practiced in sequences other than those illustrated or described herein, and that the terms "first," "second," and the like are generally used herein in a generic sense and do not limit the number of terms, e.g., the first term can be one or more than one. In addition, "and/or" in the specification and claims means at least one of connected objects, a character "/" generally means that a preceding and succeeding related objects are in an "or" relationship.
The comment displaying method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and application scenarios thereof.
Please refer to fig. 1, which is a flowchart of a comment displaying method provided by an embodiment of the present application. The method can be applied to electronic equipment, and the electronic equipment can be a mobile phone, a tablet computer, a notebook computer and the like. As shown in fig. 1, the method may include steps S1100 to S1300, which are described in detail below.
Step S1100, acquiring a first content displayed on the display interface.
The first content may be a product, information, etc. currently being browsed by the target user. The target user is a user currently browsing first contents such as products, information, etc.
In one example, after the first content displayed on the display interface is obtained, the following step of determining the target user feature matching the first content from the target user feature set based on the first content may be directly performed.
In one example, after the first content displayed on the display interface is obtained, a selection frame for selecting the ranking mode of the comments is provided; and acquiring the sorting mode selected by the target user through the selection frame, and executing the following step of determining the target user characteristics matched with the first content from the target user characteristic set based on the first content under the condition that the selected sorting mode is the personalized sorting mode.
It can be understood that, when the selected sorting manner is the default sorting manner, the comment corresponding to the first content may be directly displayed according to the attribute information of the comment, for example, the sorting information of the comment corresponding to the first content may be determined according to the attribute information, and then the comment corresponding to the first content may be displayed according to the sorting information. The attribute information of the comment is, for example, but not limited to, the posting time of the comment, the number of likes of the comment, and the like.
For example, as shown in fig. 2, after the display interface displays the basketball game information of which the first content is "team a VS team B", the electronic device may provide a selection box for selecting a ranking mode of the comments, where the following step of determining the target user feature matching the first content from the target user feature set based on the first content may be performed after the target user selects "personalized ranking". Alternatively, as shown in fig. 3, when the target user selects "default ranking", the comments corresponding to the basketball game information, for example, 5 comments, may be ranked according to the posting time of the comments, and the comments corresponding to the basketball game information, for example, the 5 comments, may be displayed.
For example, after the display interface displays the basketball game information of "team a VS team B", the electronic device may not provide a selection interface for selecting a ranking method, but directly perform the following steps of determining a target user feature matching the first content from the target user feature set based on the first content, and ranking the comments corresponding to the basketball game information, for example, 5 comments, according to the basketball game information and the target user feature, so as to display the comments corresponding to the basketball game information, for example, the 5 comments.
After acquiring the first content displayed by the display interface, entering:
step S1200, based on the first content, determines a target user feature matching the first content from the target user feature set.
The target user feature set is a user feature set corresponding to the target user, and the target user feature set is a set formed by user features which are collected in advance based on the historical behaviors of the target user and reflect the preference of the target user. The target user feature set comprises a plurality of target user features, each target user feature comprises a first user feature and a second user feature, the second user feature is a sub-feature under the first user feature, and each first user feature can correspond to a plurality of second user features.
The above first user feature is a coarse-grained user feature, which can reflect the preference of the target user to some extent. Illustratively, the first user characteristic may include: basketball, music, entertainment and fun.
The second user characteristic is a fine-grained user characteristic of a relatively coarse-grained user characteristic, and can well reflect the preference of the target user. It will be appreciated that the coarse-grained user features and the fine-grained user features are relative, for example, if a user is interested in basketball, and the team supported is team B, then basketball is relatively the coarse-grained user features, i.e., the first user features, and team B is the fine-grained user features, i.e., the second user features. Illustratively, the second user characteristic may include: team B, team C, artist 1, artist 2, actor a, actor B, actor C, comedy talk show, phase association, phase actor.
Illustratively, the set of target user characteristics includes a plurality of target user characteristics, which may be basketball, [ B team's players, C team's players ], music: [ singer 1, singer 2], entertainment: [ actor a, actor B, actor C ], fun: [ comedy talk show, audio consortium, audio actor ], where the set of target user characteristics may be: { basketball, [ players of team B, and team C ]; music [ singer 1, singer 2 ]; entertainment, namely actor a, actor b and actor c; the person is funny [ comedy talk show, phase sound association, phase sound actor ] }.
Continuing with the above example, after the display interface displays the basketball game information of "team a VS team B", the electronic device determines the target user characteristics matching the basketball game information of "team a VS team B" from the target user characteristics set.
In this embodiment, the step S1200, based on the first content, of determining the target user characteristic matched with the first content from the target user characteristic set may further include the following steps S1210 to S1220:
in step S1210, a category tag of the first content is acquired.
The category tag of the first content may be a category tag to which a subject name of the first content belongs.
Continuing with the example above, the basketball game information "team A VS team B" is labeled "basketball".
Step S1220, determining a target user feature matching the first content according to the category label of the first content and the target user feature set.
In this step S1220, determining the target user characteristics matching the first content according to the category label and the target user characteristic set of the first content may further include the following steps S1221 to S1222:
and step S1221, obtaining alternative target user characteristics according to the category label of the first content and the target user characteristic set.
In step S1221, since the first user characteristic of the target user is collected according to the category label of the first content, the first user characteristic of the target user and the category label of the first content adopt the same set of label system, and character matching can be directly performed.
Continuing with the above example, the first content displayed on the display interface is basketball match information of "team A, team VS, B", and since the category label of the basketball match information is "basketball", the category label of the basketball match information "basketball" is character-matched with the target user feature set { basketball: [ players of team B, team C ], music: [ singer 1, singer 2], entertainment: [ actor a, actor B, actor C ], fun: [ comedy talk show, voice association, voice actor ] }, to obtain the alternative target user feature "basketball: [ team B, team C ]".
Step S1222, obtaining a second user characteristic satisfying the third condition from the candidate target user characteristics, and obtaining the processed candidate target user characteristic as the determined target user characteristic matched with the first content.
The third condition includes: the similarity score between the second user characteristic and the first content is larger than a set second score threshold value. The set second score threshold may be a value set according to an actual application scenario and an actual demand.
In step S1222, after the candidate target user feature is obtained, since the content name of the first content does not necessarily contain all the second user features in the candidate target user feature, the content name of the first content is also matched with the second user features in the candidate target user feature, so as to filter the second user features, that is, fine-grained user features, in the candidate target user feature.
In step S1222, when the second user feature of the candidate target user features satisfies the third condition, the second user feature is retained, and the retained second user feature and the first user feature are combined to form the target user feature matching the first content.
In step S1222, a semantic relevance score between the content name of the first content and each second user feature in the candidate target user features may be calculated by using a sieme-LSTM (Long Short-Term Memory) model, as a similarity score between the content name of the first content and each second user feature in the candidate target user features, where the similarity score is usually a numerical value between 0 and 1, and a closer similarity score to 1 indicates a more semantic relevance between the second user feature and the content name of the first content. And then comparing the similarity score between the calculated content name of the first content and each second user feature in the candidate target user features with a second score threshold, retaining the corresponding second user feature when the similarity score is greater than the second score threshold, and filtering the corresponding second user feature when the similarity score is less than or equal to the second score threshold.
Continuing with the above example, the candidate target user feature is "basketball, [ players of team B, players of team C ]", and the topic name of the information of the basketball game information is "team a VS team B", similarity scores between team B "," players of team C ", and team a VS team B" are calculated, respectively, a similarity score p1 between team B and team a VS team B ", a similarity score p2 between team B" and team a VS team B ", and a similarity score p3 between team C" and team a VS team B ", and the similarity score p3 between team C" and team a "VS team B" is smaller than a second score threshold, where, the team C filtered player in the candidate target user feature retains team B and team B players in the candidate target user feature, get "basketball" [ players of team B, team B ] "" as the target user characteristics matching the basketball match information "" team A VS team B "".
According to the steps S1221 to S1222, the target user feature set is semantically matched with the first content, and redundant user features that do not conform to the first content are filtered out, so that the data processing efficiency is improved.
After determining a target user characteristic matching the first content from the target user characteristic set based on the first content, entering:
and step S1300, displaying the comment corresponding to the first content according to the first content and the target user characteristics.
In this embodiment, after determining the target user feature matching the first content from the target user feature set based on the first content according to the above step S1200, the comment corresponding to the first content may be displayed according to the first content and the target user feature according to the step S1300.
In this embodiment, in this step S1300, displaying the comment corresponding to the first content according to the first content and the target user characteristic may further include the following steps S1331 to S1335:
step S1331, obtaining a user association degree score between the comment corresponding to the first content and the target user feature.
The user relevancy score can reflect the relevance between the comment and the target user characteristics. The user relevancy score is typically a number between 0 and 1, where a higher user relevancy score indicates that the comment is more relevant to the target user characteristic.
In this step S1331, a user association score between the comment corresponding to the first content and the target user feature may be obtained according to a preset first model. The input of the preset first model is any comment and target user characteristic corresponding to the first content, and the output is the calculated user association degree score between the comment and the target user characteristic. The preset first model may be a neural network model, such as but not limited to a bp (back propagation) neural network model, a convolutional neural network model, and the like, and the preset first model is not specifically limited herein.
Continuing with the above example, the target user characteristic matching the basketball game information "team A VS team B" is "basketball: [ players of team B, team B ]", where a correlation score between the comment corresponding to the basketball game information and "basketball: [ players of team B, team B ]". For example, the comments 1 [ refuel in team A!are calculated separately using a preset first model! Support you! The relevancy score between "basketball: [ team B, player of team B ]" serves as the user relevancy score for comment 1, and comment 2 [ team B general champion! | A An association score between "basketball: [ team B, player of team B ]", as a user association score for comment 2, etc., indicates that comment 2 is more relevant to the target user feature if the user association score for comment 2 is higher than the user association score for comment 1.
Step S1332, obtaining a content association degree score between the comment corresponding to the first content and the first content.
The content relevancy score can reflect a correlation between the review and the first content. The content relevance score is typically a number between 0 and 1, wherein a higher content relevance score indicates that the piece of review is more relevant to the first content.
In this step S1332, a content association degree score between the comment corresponding to the first content and the first content may be obtained according to a preset second model. The input of the preset second model is any comment and first content corresponding to the first content, and the output is the calculated content relevancy score between the comment and the first content. The preset second model may be a neural network model, such as but not limited to a bp (back propagation) neural network model, a convolutional neural network model, and the like, and the preset second model is not specifically limited herein.
Continuing with the above example, the target user characteristic matching the basketball game information "team A VS team B" is "basketball: [ players of team B, team B ]", where a correlation score between the comment corresponding to the basketball game information and the basketball game information is calculated. For example, the comments 1 [ refuel in team A!are calculated separately using a preset second model! Support you! The association score between the basketball game information is used as the content association score of comment 1, comment 2 [ the general champion of team B! | A The relevancy score between basketball game information is used as the content relevancy score of the comment 2, and the comment 3 [? The association score between the basketball game information is used as the content association score of the comment 3, and if the relevance between the comment 3 and the basketball game information is not high, the quality of the comment 3 is not high.
It should be noted that, a Listwise method may be adopted to train the preset first model and the preset second model respectively, in this way, during the training process, the model can receive multiple comments at one time, and the model better distinguishes whether the comments are good or bad through comparative learning of different comments. In an actual scene, ranking labels can be constructed according to the fact that the user likes or browses for a long time and the like according to the comments in the comment set, the data are used for training the model, and the output result of the model is consistent with the ranking labels as much as possible through the training model.
According to the step S1332, since the comment really wanted to be seen by the user cannot be completely screened out only by the relevance between the comment and the user feature, the comment is scored according to the dimension of the quality of the comment, and the better the content and the information of the comment are, the higher the quality is, the higher the score is; while the scores for the irrigated reviews, unrelated reviews, and unintelligible reviews may be lower.
And S1333, fusing the user relevance scores and the content relevance scores of the comments corresponding to the first content to obtain the target scores of the comments corresponding to the first content.
In this embodiment, in this step S1333, fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain the target score of the comment corresponding to the first content may further include the following steps S1333-1 to S1333-3:
and S1333-1, fusing the user relevance scores and the content relevance scores of the comments corresponding to the first content to obtain a fusion score of the comments corresponding to the first content.
In this step S1333-1, the user relevance Score and the content relevance Score of each comment may be linearly fused to obtain a target Score of each comment, where the user relevance Score and the content relevance Score of the comment may be fused by using the following linear function to obtain a target Score of the ith commenti：
Scorei＝Si1*m+Si2*(1-m) (1)
Wherein i is an integer from 1 to n, n is the total number of comments corresponding to the first content, and Si1Is the user relevancy score of the ith comment calculated according to the above step S1331, Si2For the content relevance score of the ith comment calculated according to the above step S1332, m is a parameter value smaller than 1, which represents the degree of importance of the user relevance score.
According to the step S1333, the merged score is used as the final ranking basis, so that the connection between the comment and the user feature and the quality of the comment are comprehensively considered.
And S1333-2, obtaining the emotion polarity score of the comment corresponding to the first content.
The sentiment polarity of the comment indicates whether the preferred sentiment represented by the comment is positive or negative. The emotion polarity score of the comment is generally a numerical value between 0 and 1, the higher the emotion polarity score of the comment is, the higher the probability that the emotion polarity representing the comment is positive, and the lower the emotion polarity score of the comment is, the higher the probability that the emotion polarity representing the comment is negative.
In step S1333-2, the sentiment polarity score of the comment may be calculated by using a bidirectional LSTM model, where the input of the bidirectional LSTM model is the comment and the output is the sentiment polarity score of the comment.
And S1333-3, obtaining a target score of the comment corresponding to the first content according to the emotional polarity score and the fusion score of the comment corresponding to the first content.
In this step S1333-3, the target score final of the ith comment can be calculated using the following formulai：
finali＝Scorei*Si3 (2)
Wherein, ScoreiFusion score, S, representing the ith commenti3The sentiment polarity score of the ith comment is represented.
According to this step S1333-3, it calculates the emotion polarity score of the comment, and performs a depreciation process on the comment that has a high correlation with the user 'S feature but does not meet the user' S preference in emotion polarity.
Step S1334, determining ranking information of the comment corresponding to the first content according to the target score of the comment corresponding to the first content.
In this embodiment, after the target score of the comment corresponding to the first content is obtained according to step S1333, the comment corresponding to the first content may be sorted according to step S1334, and the sorting information of the comment corresponding to the first content is obtained.
Continuing with the above example, as shown in FIG. 3, the above review set of basketball game information includes 5 reviews [ refuel on team A! Support you! Is { the comment area on what? { I feel that team A wins }, { I want team B to win }, { team B general champion! | A J, wherein, as shown in fig. 2, the comment posted by the user 5 [ B team general champion | ]. | A The goal score is greater than the goal score for the comment posted by user 4 [ i want team B to win ], the goal score for the comment posted by user 4 [ i want team B to win ] is greater than the comment posted by user 1 [ team a refuel! Support you! Target score, comment made by user 1 [ refuel team a! Support you! An objective score greater than the comment posted by user 3 [ i feel that team a is won ], and an objective score greater than the comment posted by user 2 [ i feel that team a is won ]? Target score. Here, the 5 comments corresponding to the basketball game information are ranked according to the target scores of the 5 comments, and the obtained ranking information is, for example, a ranking label [ 3,5,4,2,1 ].
And step S1335, displaying the comments corresponding to the first content according to the sorting information.
In this embodiment, after the ranking information of the comment corresponding to the first content is obtained according to the above step S1334, the comment corresponding to the first content can be displayed according to the ranking information according to the step S1335.
Continuing with the above example, the 5 comments [ team A plus oil! Support you! Is { the comment area on what? { I feel that team A wins }, { I want team B to win }, { team B general champion! | A The corresponding ranking information, for example, the ranking label is [ 3,5,4,2,1 ], where the 5 comments corresponding to the basketball game information are shown according to the ranking label [ 3,5,4,2,1 ] as shown in fig. 2.
In the embodiment of the application, after first content displayed on a display interface is acquired, a target user characteristic matched with the first content is determined from a target user characteristic set based on the first content, and a comment corresponding to the first content is displayed according to the first content and the target user characteristic. The target user characteristics comprise the first user characteristics and the second user characteristics, and the second user characteristics are sub-characteristics under the first user characteristics, so that the second user characteristics can better reflect the favor of the target user to the comments, namely, the comments can be distinguished according to the favor of the user in a finer granularity mode, the comments concerned by the user and the interested comments can be arranged in front, and personalized comment display is realized.
It should be noted that, in the comment displaying method provided in the embodiment of the present application, the execution subject may be a comment displaying apparatus, or a control module in the comment displaying apparatus, which is used for executing the comment displaying method. The comment displaying method performed by the comment displaying device is taken as an example in the embodiment of the application, and the comment displaying method provided by the embodiment of the application is described.
In one embodiment, before determining, according to the above step S1200, a target user feature matching the first content from the target user feature set, the comment displaying method further includes a step of obtaining the target user feature set, where obtaining the target user feature set may further include the following steps S1010 to S1040:
step S1010, a first user characteristic of the target user and text information corresponding to the target user are obtained.
For example, as shown in fig. 4, when registering any xx application, the target user may select a category tag of interest in the xx application, where the category tag may be referred to as a first user characteristic of the target user, because the category tag selected by the target user is: basketball, music, entertainment, fun, here, the first user characteristic of the target user may be: basketball, music, entertainment and fun.
For another example, when the target user browses information by using the xx application, the category label of the information frequently browsed by the target user may be used as the first user characteristic, and if the category label of the information frequently browsed by the target user is funny, the first user characteristic may be: the human body is funed.
Illustratively, the first user characteristics collected for the target user include { basketball, music, entertainment, fun }.
The text information corresponding to the target user is the text information which is interested by the target user. The text information corresponding to the target user includes the comment and/or the second content, which may be, for example, the comment and/or the second content that is of interest to the target user. Moreover, the second content and the first content have the same category attribute, for example, when the first content is information, the text information corresponding to the target user may include comments and/or history information that the target user is interested in, and specifically, may be comments and/or history information that the target user approves.
Illustratively, as shown in fig. 5, after the target user enters the comment interface, the comments "general champion of B team" (comment made by user 5) and "4-2A team" of B team (comment made by user 6) (the black circle in the figure indicates that the comment is liked) that are liked by the target user may be taken as the text information that is of interest to the target user.
Step S1020, classifying the text information according to the first category label of the first user characteristic, and obtaining a second category label of the text information.
The first category label for the first user characteristic may be a name of the first user characteristic. Illustratively, the collected first user characteristics of the target user include { basketball, music, entertainment, and fun }, where the first category labels of the first user characteristics are { basketball, music, entertainment, and fun }, respectively.
In step S1020, after the text information corresponding to the target user is collected, the text information is classified according to the first category label of the first user characteristic, and different categories of text information are aggregated to obtain a second category label of the text information. Illustratively, the second category tag of the text information may be { basketball, music, entertainment, fun }, for example, the category tags of the above text information "team general champion" and "team B4-2A" are both "basketball".
And step S1030, obtaining a second user characteristic of the target user according to the second category label.
In this embodiment, the obtaining of the second user characteristic of the target user according to the second category label in step S1030 may further include steps S1030-1 to S1030-3:
and step S1030-1, obtaining the keywords meeting the first condition from the second category label to obtain the alternative keywords corresponding to the second category label.
In step S1030-1, a keyword in the text information included in each second category tag may be extracted, a corresponding relationship between the keyword and the second category tag is established, and then the keyword satisfying the first condition is obtained from the second category tag to obtain an alternative keyword corresponding to the second category tag.
Continuing the above example, respectively extracting keywords from the text messages "team B general champion" and "team B4-2A", and extracting keywords: the second category labels of the text information "total champions of B team" and "4-2A team" of B team are: basketball, here, keywords: "team B" and "team A" are keywords in the second category label.
The first condition includes: the co-occurrence ratio of the keywords is larger than a set ratio threshold. The co-occurrence ratio of the keywords is equal to the ratio of the number of the text messages containing the keywords under the second category label to the total number of the text messages containing the keywords under the second category label, and specifically, the co-occurrence ratio x of the qth keyword contained under the jth second category label can be calculated according to the following formulaq：
Figure BDA0003016670360000131
Wherein, wqIndicates the number of text messages containing the q-th keyword under the jth second category label, wGeneral assemblyRepresenting the total amount of text information under the jth second category label.
The set duty threshold may be a value set according to an actual application scenario and an actual demand.
In step S1030-1, when the keyword in the second category label satisfies the above first condition, the keyword is retained, and the retained keyword is used as the candidate keyword in the second category label.
In step S1031, it calculates the co-occurrence ratio of the keywords in each second category label. And then comparing the calculated co-occurrence ratio of the keywords with a set ratio threshold, keeping the corresponding keywords when the co-occurrence ratio is greater than the set ratio threshold, and filtering the corresponding keywords when the co-occurrence ratio is less than or equal to the set ratio threshold.
Continuing with the above example, keyword extraction is respectively performed on the text information "general champion of team B" and "team B4-2A", and the extracted keywords are: and B team and A team respectively calculate the co-occurrence ratio of the B team and the A team, and the co-occurrence ratio of the A team is smaller than a set ratio threshold, so that the A team is filtered out, and only the B team is reserved.
And step S1030-2, acquiring keywords meeting a second condition from the alternative keywords to obtain the processed alternative keywords.
The second condition includes: the emotion polarity score of the keyword is larger than a set first score threshold value. The emotion polarity score of the keyword is equal to the average value of the emotion polarity scores of the text messages containing the keywords under the second category label, and specifically, the emotion polarity score x of the q-th keyword contained under the jth second category label can be calculated by using the following formulaq：
Figure BDA0003016670360000141
Wherein m represents the number of text messages containing the q-th keyword under the jth second category label,
Figure BDA0003016670360000142
the emotion polarity score of the pth text message containing the qth keyword under the jth second category tag can be calculated by using the following formula
Figure BDA0003016670360000143
Figure BDA0003016670360000144
When the p text information of the q-th keyword has obvious user preference characteristics, the emotion polarity score of the text information is 1, for example, the text information complied with the user preference basically can be judged; otherwise, calculating the emotion polarity score of the text information by adopting a bidirectional LSTM model, wherein the emotion polarity score closer to 1 indicates that the text information has higher possibility of being positive emotion.
The set first score threshold may be a numerical value set according to an actual application scenario and an actual demand.
According to the step, the average value of the emotion polarity scores of all text messages containing the keyword in the second category label is used as the emotion polarity score of the keyword, the keywords with the emotion polarity scores smaller than or equal to the set first score threshold value are filtered, and the keywords with the emotion polarity scores larger than the set first score threshold value are reserved as the final second user characteristics.
And S1030-3, acquiring second user characteristics of the target user according to the processed alternative keywords.
Illustratively, the second user characteristic corresponding to the second category label "basketball" is { team B, team C }, the second user characteristic corresponding to the second category label "music" is { singer 1, singer 2}, the second user characteristic corresponding to the second category label "entertainment" is { actor a, actor B, actor C }, and the second user characteristic corresponding to the second category label "fun" is { comedy talk show, vocal association, vocal actor }.
In step S1030-3, the keyword whose emotion polarity score is greater than the set first score threshold is retained as the final second user feature.
Step S1040, according to the first category label and the second category label, the first user feature and the second user feature are combined to obtain a target user feature set.
Illustratively, according to a first category label { basketball, music, entertainment, fun } and a second category label { basketball, music, entertainment, fun }, a first user characteristic { basketball, music, entertainment, fun } and a second user characteristic { team B, team C, artist 1, artist 2, actor a, actor B, actor C, comedy talk show, vocal consortium, vocal actors } are combined to obtain a plurality of target user characteristics, which may be basketball: [ team B, team C, artist: [ artist 1, artist 2], entertainment: [ actor a, actor B, actor C ], fun: [ comedy talk show, vocal consortium, vocal actors ], where the set of target user characteristics may be: { basketball, [ players of team B, and team C ]; music [ singer 1, singer 2 ]; entertainment [ actor a, actor b, actor c ]; the person is funny [ comedy talk show, phase sound association, phase sound actor ] }.
According to the embodiment of the disclosure, emotion analysis and content understanding can be performed on historical behaviors of the target user, so that a first user characteristic and a second user characteristic which accord with user preferences are mined, and a target user characteristic set is constructed.
Next, taking the first content as information as an example, a comment displaying method of an example is shown, as shown in fig. 6, in this example, the comment displaying method includes the following steps:
step S6010, information displayed on the display interface is obtained.
In step S6020, the display interface provides a selection box for selecting a ranking mode of the comments, and if the ranking mode of the comments selected by the user is a personalized ranking mode, the following step S6030 is performed, and otherwise, the following step S6070 is performed.
Step S6030, a target user feature set is acquired.
The target user is a user browsing the information, and the target user characteristics comprise a first user characteristic and a second user characteristic.
In step S6030, first user characteristics of the target user and text information corresponding to the target user may be obtained, and the text information may be classified according to a first category label of the first user characteristics to obtain a second category label of the text information; then extracting keywords in the text information, and establishing a corresponding relation between the keywords and the second category label; meanwhile, obtaining keywords meeting the first condition from each second category label to obtain alternative keywords of each second category label, and obtaining keywords meeting the second condition from each alternative keyword to obtain processed alternative keywords; and finally, according to each processed alternative keyword, obtaining a second user characteristic of the target user, and according to the first category label and the second category label, combining the first user characteristic and the second user characteristic to obtain a target user characteristic set.
Step S6040, according to the information displayed on the display interface, the target user characteristic matched with the information is determined from the target user characteristic set.
In step S6040, the category label of the information may be matched with the first user feature in the target user feature set to obtain the first user feature and each second user feature that conform to the information; secondly, the content of the information is matched with each second user characteristic, the second user characteristics related to the information content semantics are screened out, and the first user characteristics which accord with the information and the second user characteristics reserved after screening are combined to be used as target user characteristics matched with the information.
Step S6050, determining the target score of the comment corresponding to the information according to the information and the matched target user characteristics.
In step S6050, a user association score between the comment corresponding to the information and the target user feature may be obtained according to a preset first model, a content association score between the comment corresponding to the information and the content of the information may be obtained according to a preset second model, and the user association score and the content association score of each comment are fused to obtain a fusion score of each comment; and then obtaining the emotion polarity score of each comment, and further obtaining the target score of each comment according to the emotion polarity score of each comment and the corresponding fusion score.
Step S6060, determining the ranking information of the comments corresponding to the information according to the target scores of the comments corresponding to the information, and executing step S6080.
Step S6070, according to the attribute information of the comment, determining the ranking information of the comment corresponding to the information.
Step S6080, the comments corresponding to the information are displayed according to the sorting information.
Corresponding to the above embodiment, referring to fig. 7, an embodiment of the present application further provides a comment displaying apparatus 700, including:
an obtaining module 7100, configured to obtain first content displayed by the display interface.
A determining module 7200, configured to determine, based on the first content, a target user feature that matches the first content from a target user feature set; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic.
A displaying module 7300, configured to display, according to the first content and the target user characteristic, a comment corresponding to the first content.
In one embodiment, the obtaining module 7100 is further configured to: acquiring first user characteristics of a target user and text information corresponding to the target user; wherein the text information comprises comments and/or second content; classifying the text information according to a first class label of the first user characteristic to obtain a second class label of the text information; obtaining a second user characteristic of the target user according to the second category label; and combining the first user characteristic and the second user characteristic according to the first category label and the second category label to obtain the target user characteristic set.
In an embodiment, the obtaining module 7100 is specifically configured to: acquiring keywords meeting a first condition from the second category labels to obtain alternative keywords corresponding to the second category labels; acquiring keywords meeting a second condition from the alternative keywords to obtain processed alternative keywords; and obtaining a second user characteristic of the target user according to the processed alternative keywords.
The first condition includes: the co-occurrence ratio of the keywords is greater than a set ratio threshold, and the co-occurrence ratio of the keywords is equal to the ratio of the number of the text messages containing the keywords under the second type label to the total number of the text messages under the second type label.
The second condition includes: the emotion polarity scores of the keywords are larger than a set first score threshold, and the emotion polarity scores of the keywords are equal to the average value of the emotion polarity scores of the text information containing the keywords under the second type of tags.
In an embodiment, the determining module 7300 is specifically configured to: acquiring a user association degree score between the comment corresponding to the first content and the target user characteristic; acquiring a content association degree score between the comment corresponding to the first content and the first content; fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a target score of the comment corresponding to the first content; determining ranking information of the comment corresponding to the first content according to the target score of the comment corresponding to the first content; and displaying the comments corresponding to the first content according to the sorting information.
In an embodiment, the determining module 7300 is specifically configured to: fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a fused score of the comment corresponding to the first content; obtaining the emotion polarity fraction of the comment corresponding to the first content; and obtaining a target score of the comment corresponding to the first content according to the emotional polarity score and the fusion score of the comment corresponding to the first content.
The comment displaying device in the embodiment of the application may be a device, or may be a component, an integrated circuit, or a chip in a terminal. The device can be mobile electronic equipment or non-mobile electronic equipment. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the embodiments of the present application are not particularly limited.
The comment presentation device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, and embodiments of the present application are not limited specifically.
The comment displaying device provided by the embodiment of the application can realize each process realized by the method embodiment, and is not repeated here to avoid repetition.
Corresponding to the above embodiments, optionally, as shown in fig. 8, an electronic device 800 is further provided in the embodiments of the present application, and includes a processor 801, a memory 802, and a program or an instruction stored in the memory 802 and capable of running on the processor 801, where the program or the instruction is executed by the processor 801 to implement each process of the above comment displaying method embodiment, and can achieve the same technical effect, and no further description is provided here to avoid repetition.
It should be noted that the electronic device in the embodiment of the present application includes the mobile electronic device and the non-mobile electronic device described above.
Fig. 9 is a schematic diagram of a hardware structure of an electronic device implementing an embodiment of the present application.
The electronic device 900 includes, but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909, and a processor 910.
Those skilled in the art will appreciate that the electronic device 900 may further include a power source (e.g., a battery) for supplying power to various components, and the power source may be logically connected to the processor 910 through a power management system, so as to manage charging, discharging, and power consumption management functions through the power management system. The electronic device structure shown in fig. 9 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than those shown, or combine some components, or arrange different components, and thus, the description is not repeated here.
The processor 910 is configured to obtain first content displayed by a display interface; determining a target user characteristic matched with the first content from a target user characteristic set based on the first content; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic; and displaying the comment corresponding to the first content according to the first content and the characteristics of the target user.
In one embodiment, the processor 910 is further configured to obtain a first user characteristic of a target user and text information corresponding to the target user; wherein the text information comprises comments and/or second content; classifying the text information according to a first class label of the first user characteristic to obtain a second class label of the text information; obtaining a second user characteristic of the target user according to the second category label; and combining the first user characteristic and the second user characteristic according to the first category label and the second category label to obtain the target user characteristic set.
In an embodiment, the processor 910 is further configured to obtain a keyword meeting a first condition from the second category tag, to obtain an alternative keyword corresponding to the second category tag; acquiring keywords meeting a second condition from the alternative keywords to obtain processed alternative keywords; and obtaining a second user characteristic of the target user according to the processed alternative keywords.
In one embodiment, the processor 910 is further configured to obtain a user association score between the comment corresponding to the first content and the target user characteristic; acquiring a content association degree score between the comment corresponding to the first content and the first content; fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a target score of the comment corresponding to the first content; determining ranking information of the comment corresponding to the first content according to the target score of the comment corresponding to the first content; and displaying the comments corresponding to the first content according to the sorting information.
In one embodiment, the processor 910 is further configured to fuse the user relevance score and the content relevance score of the comment corresponding to the first content, and obtain a fused score of the comment corresponding to the first content; obtaining the emotion polarity fraction of the comment corresponding to the first content; and obtaining a target score of the comment corresponding to the first content according to the emotional polarity score and the fusion score of the comment corresponding to the first content.
It should be understood that, in the embodiment of the present application, the input Unit 904 may include a Graphics Processing Unit (GPU) 9041 and a microphone 9042, and the Graphics Processing Unit 9041 processes image data of a still picture or a video obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The display unit 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 907 includes a touch panel 9071 and other input devices 9072. A touch panel 9071 also referred to as a touch screen. The touch panel 9071 may include two parts, a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail herein. Memory 909 can be used to store software programs as well as various data including, but not limited to, application programs and operating systems. The processor 910 may integrate an application processor, which primarily handles operating systems, user interfaces, applications, etc., and a modem processor, which primarily handles wireless communications. It is to be appreciated that the modem processor described above may not be integrated into processor 910.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the comment displaying method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and so on.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, the processor is configured to run a program or an instruction, implement each process of the above comment displaying method embodiment, and achieve the same technical effect, and the details are not repeated here to avoid repetition.
It should be understood that the chips mentioned in the embodiments of the present application may also be referred to as system-on-chip, system-on-chip or system-on-chip, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatus of the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions involved, e.g., the methods described may be performed in an order different than that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (12)

1. A review presentation method, the method comprising:
acquiring first content displayed by a display interface;
determining a target user characteristic matched with the first content from a target user characteristic set based on the first content; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic;
and displaying the comment corresponding to the first content according to the first content and the characteristics of the target user.
2. The method of claim 1, wherein prior to determining the target user characteristic from the set of target user characteristics that matches the first content based on the first content, further comprising:
acquiring first user characteristics of a target user and text information corresponding to the target user; wherein the text information comprises comments and/or second content;
classifying the text information according to a first class label of the first user characteristic to obtain a second class label of the text information;
obtaining a second user characteristic of the target user according to the second category label;
and combining the first user characteristic and the second user characteristic according to the first category label and the second category label to obtain the target user characteristic set.
3. The method of claim 2, wherein obtaining the second user characteristic of the target user according to the second category label comprises:
acquiring keywords meeting a first condition from the second category labels to obtain alternative keywords corresponding to the second category labels; wherein the first condition comprises: the co-occurrence ratio of the keywords is greater than a set ratio threshold, and the co-occurrence ratio of the keywords is equal to the ratio of the number of the text messages containing the keywords under the second type label to the total number of the text messages under the second type label;
acquiring keywords meeting a second condition from the alternative keywords to obtain processed alternative keywords; wherein the second condition comprises: the emotion polarity score of the keyword is greater than a set first score threshold value, and the emotion polarity score of the keyword is equal to the average value of the emotion polarity scores of the text information containing the keyword under the second type of label;
and obtaining a second user characteristic of the target user according to the processed alternative keywords.
4. The method of claim 1, wherein said presenting comments corresponding to the first content according to the first content and the target user characteristics comprises:
acquiring a user association degree score between the comment corresponding to the first content and the target user characteristic;
acquiring a content association degree score between the comment corresponding to the first content and the first content;
fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a target score of the comment corresponding to the first content;
determining ranking information of the comment corresponding to the first content according to the target score of the comment corresponding to the first content;
and displaying the comments corresponding to the first content according to the sorting information.
5. The method according to claim 4, wherein the fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain the target score of the comment corresponding to the first content further comprises:
fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a fused score of the comment corresponding to the first content;
obtaining the emotion polarity fraction of the comment corresponding to the first content;
and obtaining a target score of the comment corresponding to the first content according to the emotional polarity score and the fusion score of the comment corresponding to the first content.
6. A comment presenting apparatus, comprising:
the acquisition module is used for acquiring first content displayed by a display interface;
a determining module, configured to determine, based on the first content, a target user feature that matches the first content from a target user feature set; the target user characteristics comprise a first user characteristic and a second user characteristic, and the second user characteristic is a sub-characteristic under the first user characteristic;
and the display module is used for displaying the comments corresponding to the first content according to the first content and the characteristics of the target user.
7. The apparatus of claim 6, wherein the obtaining module is further configured to:
acquiring first user characteristics of a target user and text information corresponding to the target user; wherein the text information comprises comments and/or second content;
classifying the text information according to a first class label of the first user characteristic to obtain a second class label of the text information;
obtaining a second user characteristic of the target user according to the second category label;
and combining the first user characteristic and the second user characteristic according to the first category label and the second category label to obtain the target user characteristic set.
8. The apparatus of claim 7, wherein the obtaining module is specifically configured to:
acquiring keywords meeting a first condition from the second category labels to obtain alternative keywords corresponding to the second category labels; wherein the first condition comprises: the co-occurrence ratio of the keywords is greater than a set ratio threshold, and the co-occurrence ratio of the keywords is equal to the ratio of the number of the text messages containing the keywords under the second type label to the total number of the text messages under the second type label;
acquiring keywords meeting a second condition from the alternative keywords to obtain processed alternative keywords; wherein the second condition comprises: the emotion polarity score of the keyword is greater than a set first score threshold value, and the emotion polarity score of the keyword is equal to the average value of the emotion polarity scores of the text information containing the keyword under the second type of label;
and obtaining a second user characteristic of the target user according to the processed alternative keywords.
9. The device of claim 6, wherein the display module is specifically configured to:
acquiring a user association degree score between the comment corresponding to the first content and the target user characteristic;
acquiring a content association degree score between the comment corresponding to the first content and the first content;
fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a target score of the comment corresponding to the first content;
determining ranking information of the comment corresponding to the first content according to the target score of the comment corresponding to the first content;
and displaying the comments corresponding to the first content according to the sorting information.
10. The device of claim 9, wherein the display module is specifically configured to:
fusing the user relevance score and the content relevance score of the comment corresponding to the first content to obtain a fused score of the comment corresponding to the first content;
obtaining the emotion polarity fraction of the comment corresponding to the first content;
and obtaining a target score of the comment corresponding to the first content according to the emotional polarity score and the fusion score of the comment corresponding to the first content.
11. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of the comment presentation method of any one of claims 1-5.
12. A readable storage medium, characterized in that the readable storage medium stores thereon a program or instructions which, when executed by a processor, implement the steps of the comment presentation method of any one of claims 1 to 5.
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