CN108549641B - Song evaluation method, device, equipment and storage medium - Google Patents
Song evaluation method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a song evaluation method, a song evaluation device, song evaluation equipment and a song storage medium, wherein the method comprises the following steps: acquiring evaluation information of a target song; and determining whether the target song is good or not based on the evaluation information and a preset song evaluation model. According to the embodiment of the invention, whether the song is good or not can be accurately evaluated under the condition of not depending on the music list information, so that reliable song evaluation information can be provided for a user, and the user can accurately find the good and good song.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a song evaluation method, a song evaluation device, song evaluation equipment and a song storage medium.
Background
Music is an indispensable part of people's daily life, and music has the efficiency of alleviating pressure, cultivating temperament and feelings and improving work efficiency, but the prerequisite that music can obtain these efficiencies is that music can satisfy people's appreciation level, and music can reach above-mentioned efficiency only when being considered good-hearing.
The existing music playing platforms are numerous, each music playing platform is provided with massive music resources, and the favorite music can be met, so that convenience is provided for users, and meanwhile, the users must sweep favorite music from massive music, the operation is difficult frequently, so that the music playing platforms can generally issue song recommendation lists or songs click lists conveniently, however, the music on the lists is often artificially controlled, namely, the music on the lists is not always good music, the music outside the lists is not always bad music, and the reference value of the music lists is limited.
Disclosure of Invention
The embodiment of the invention provides a song evaluation method, a song evaluation device, song evaluation equipment and a song storage medium, which are used for accurately evaluating whether a song is good or not under the condition of not depending on music list information.
A first aspect of an embodiment of the present invention provides a song evaluation method, including:
acquiring evaluation information of a target song;
and determining whether the target song is good or not based on the evaluation information and a preset song evaluation model.
Optionally, the obtaining of the evaluation information of the target song includes:
and crawling evaluation information of the target song from at least one music playing platform.
Optionally, the evaluation information includes user name information for issuing the evaluation information;
the determining whether the target song is good to hear based on the evaluation information and a preset song evaluation model comprises:
when the evaluation information of the target song comprises a plurality of pieces of evaluation information corresponding to the same user name information, performing data cleaning operation on the evaluation information of the target song, reserving one piece of the evaluation information, and removing other evaluation information in the evaluation information;
and determining whether the target song is good or not based on the evaluation information left after the data cleaning operation and a preset song evaluation model.
Optionally, before determining whether the target song is good to hear based on the evaluation information and a preset song evaluation model, the method further includes:
obtaining a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions;
and training to obtain a song evaluation model based on the plurality of first sample evaluation information and the plurality of second sample evaluation information.
A second aspect of an embodiment of the present invention provides a song rating apparatus, including:
the first acquisition module is used for acquiring evaluation information of the target song;
and the song evaluation module is used for determining whether the target song is good or not based on the evaluation information and a preset song evaluation model.
Optionally, the first obtaining module is specifically configured to:
and crawling evaluation information of the target song from at least one music playing platform.
Optionally, the evaluation information includes user name information for issuing the evaluation information;
the song evaluation module comprises:
the data cleaning sub-module is used for performing data cleaning operation on the evaluation information of the target song when the evaluation information of the target song comprises a plurality of pieces of evaluation information corresponding to the same user name information, reserving one of the evaluation information and removing other evaluation information from the evaluation information;
and the evaluation submodule is used for determining whether the target song is good or not based on the evaluation information left after the data cleaning operation and a preset song evaluation model.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions;
and the model training module is used for training to obtain a song evaluation model based on the plurality of first sample evaluation information and the plurality of second sample evaluation information.
A third aspect of an embodiment of the present invention provides a server, including:
a processor;
a memory for storing the processor-executable instructions;
the method of the first aspect described above may be performed when the processor executes the executable instructions.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which includes instructions, when the instructions are executed on the computer, the computer may perform the method of the first aspect.
According to the embodiment of the invention, the evaluation information of the target song is acquired; and determining whether the target song is good or not based on the acquired evaluation information and a preset song evaluation model. Therefore, whether the song is good or not can be accurately assessed without depending on the music list information, and reliable song evaluation information can be provided for the user, so that the user can accurately find the good and good song.
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 embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a song rating method provided by an embodiment of the present invention;
FIG. 2 is a flowchart of a method for performing step 102 according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a song rating apparatus 30 according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a song evaluation module 32 according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server 800 according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprises" and "comprising," and any variations thereof, in the description and claims of this invention, are intended to cover non-exclusive inclusions, e.g., a process or an apparatus that comprises a list of steps is not necessarily limited to those structures or steps expressly listed but may include other steps or structures not expressly listed or inherent to such process or apparatus.
The embodiment of the invention provides a song evaluation method. The method may be performed by a song rating apparatus. Referring to fig. 1, fig. 1 is a flowchart of a song rating method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
The comment information in this embodiment is a comment that includes the emotion of the user.
The target song referred to in this embodiment is a song to be evaluated. In different situations, the method for obtaining the evaluation information of the target song may be different, for example, for a certain music playing platform, when it evaluates the provided song, the evaluation information of the target song may be obtained from a corresponding database set by the music playing platform. For another example, when a third-party evaluation mechanism evaluates a song, evaluation information of a target song may be crawled from at least one music playing platform through a crawler technology. Of course, the two acquisition methods are only used for illustration and are not the only limitation of the present invention.
And 102, determining whether the target song is good to hear or not based on the evaluation information and a preset song evaluation model.
The song evaluation model according to this embodiment is a model that is trained in advance based on a model training method and can be used for determining whether a song is good or not based on the evaluation information of the song, and the specific form of the song evaluation model in this embodiment may be arbitrary, and is not particularly limited in this embodiment.
In fact, the present embodiment may further include a training step of the song evaluation model before step 102. Specifically, a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions may be obtained first. Furthermore, words containing positive emotions and words containing negative emotions are extracted from each first sample evaluation information and each second sample evaluation information respectively based on a preset emotion dictionary, and finally, a song evaluation model is obtained through training based on the extracted words and whether each sample evaluation information is marked with the negative emotions or the positive emotions, namely, the song evaluation model is obtained through training based on a plurality of first sample evaluation information marked as the positive emotions and a plurality of second sample evaluation information marked as the negative emotions. Of course, the training process of the song evaluation model is only a brief description for clarity, not all steps, and the detailed training process may refer to the existing model training method, which is not described herein again.
In the embodiment, the evaluation information of the target song is acquired; and determining whether the target song is good or not based on the acquired evaluation information and a preset song evaluation model. Therefore, whether the song is good or not can be accurately assessed without depending on the music list information, and reliable song evaluation information can be provided for the user, so that the user can accurately find the good and good song.
The following expansion and optimization are carried out on the basis of the above embodiments in combination with the attached drawings:
fig. 2 is a flowchart of an execution method of step 102 according to an embodiment of the present invention, in the method, the evaluation information acquired in step 101 includes user name information for issuing the evaluation information. On this basis, as shown in fig. 2, step 102 includes:
Since the preference and the appreciation level of the same user do not change for a long period of time (for example, 2 years, 3 years, etc.), the evaluation of the same song in a short period of time is often not changed, and therefore, if the evaluation information acquired in step 101 includes a plurality of pieces of evaluation information issued by the same user for the target song, any one of the plurality of pieces of information may be retained and the others may be deleted, thereby achieving the purpose of reducing the amount of calculation.
And step 1022, determining whether the target song is good to hear based on the evaluation information left after the data cleaning operation and a preset song evaluation model.
According to the method and the device, data cleaning operation is carried out on the obtained evaluation information of the target song, repetition is removed, whether the target song is good or not is determined based on the cleaned remaining evaluation information and the preset song evaluation model, the calculated amount of song evaluation can be reduced, and the evaluation efficiency is improved.
Fig. 3 is a schematic structural diagram of a song rating apparatus 30 according to an embodiment of the present invention, and as shown in fig. 3, the apparatus 30 includes:
a first obtaining module 31, configured to obtain evaluation information of a target song;
and the song evaluation module 32 is used for determining whether the target song is good to hear or not based on the evaluation information and a preset song evaluation model.
Optionally, the first obtaining module 31 is specifically configured to:
and crawling evaluation information of the target song from at least one music playing platform.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions;
and the model training module is used for training to obtain a song evaluation model based on the plurality of first sample evaluation information and the plurality of second sample evaluation information.
The embodiment can be used for executing the method of the embodiment in fig. 1, and the execution mode and the beneficial effects are similar, which are not described herein again.
Fig. 4 is a schematic structural diagram of a song evaluation module 32 according to an embodiment of the present invention, as shown in fig. 4, on the basis of the embodiment of fig. 3, the evaluation information includes user name information for issuing the evaluation information;
the song evaluation module 32 includes:
the data cleaning sub-module 321 is configured to, when the evaluation information of the target song includes multiple pieces of evaluation information corresponding to the same user name information, perform data cleaning operation on the evaluation information of the target song, reserve one of the multiple pieces of evaluation information, and remove other multiple pieces of evaluation information from the multiple pieces of evaluation information;
and the evaluation submodule 322 is configured to determine whether the target song is good to hear based on the evaluation information remaining after the data cleaning operation and a preset song evaluation model.
The embodiment can be used for executing the method of the embodiment of fig. 2, and the execution mode and the beneficial effect are similar, which are not described herein again.
An embodiment of the present invention further provides a server, including:
a processor;
a memory for storing the processor-executable instructions;
when the processor executes the executable instructions, the technical solutions of the embodiments of fig. 1 to 2 may be executed.
Specifically, fig. 5 is a schematic structural diagram of a server 800 according to an embodiment of the present invention, and as shown in fig. 5, the server may be embodied as a structure shown in the server 800. Server 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.
The processing component 802 generally controls overall operation of the server 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operation at the device 800. Examples of such data include instructions for any application or method operating on server 800, music, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
A power component 806 provides power to the various components of the server 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the server 800.
The multimedia component 808 includes a screen that provides an output interface between the server 800 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, audio component 810 includes a Microphone (MIC) configured to receive external audio signals when server 800 is in an operational mode, such as a recording mode and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 814 includes one or more sensors for providing various aspects of state assessment for the server 800. For example, sensor assembly 814 may detect an open/closed state of device 800, the relative positioning of components, such as a display and keypad of server 800, sensor assembly 814 may also detect a change in position of server 800 or a component of server 800, the presence or absence of user contact with server 800, orientation or acceleration/deceleration of server 800, and a change in temperature of server 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the server 800 and other devices in a wired or wireless manner. The server 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the server 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the server 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
An embodiment of the present invention further provides a computer-readable storage medium, which includes instructions, and when the instructions are executed on the computer, the computer may execute the technical solutions in the embodiments of fig. 1 to fig. 2.
Finally, it should be noted that, as one of ordinary skill in the art will appreciate, all or part of the processes of the methods of the embodiments described above may be implemented by hardware related to instructions of a computer program, where the computer program may be stored in a computer-readable storage medium, and when executed, the computer program may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a Random Access Memory (RAM), or the like.
Each functional unit in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. A song rating method, comprising:
acquiring evaluation information of a target song from a database of a music platform; acquiring a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions, extracting positive emotion words from the first sample evaluation information based on a preset emotion dictionary, and extracting negative emotion words from the second sample evaluation information;
determining whether the target song is good to hear or not based on the evaluation information and a preset song evaluation model;
the preset song evaluation model is obtained based on words containing positive emotions, words containing negative emotions and emotion information training with evaluation information marked;
the evaluation information comprises user name information for releasing the evaluation information;
the determining whether the target song is good to hear based on the evaluation information and a preset song evaluation model comprises:
when the evaluation information of the target song comprises a plurality of pieces of evaluation information corresponding to the same user name information, performing data cleaning operation on the evaluation information of the target song, reserving one piece of the evaluation information, and removing other evaluation information in the evaluation information;
and determining whether the target song is good or not based on the evaluation information left after the data cleaning operation and a preset song evaluation model.
2. A song rating apparatus, comprising:
the first acquisition module is used for acquiring evaluation information of a target song from a database of the music platform; acquiring a plurality of first sample evaluation information marked as having positive emotions and a plurality of second sample evaluation information marked as having negative emotions, extracting positive emotion words from the first sample evaluation information based on a preset emotion dictionary, and extracting negative emotion words from the second sample evaluation information;
the song evaluation module is used for determining whether the target song is good or not based on the evaluation information and a preset song evaluation model, and the preset song evaluation model is obtained based on words containing positive emotions, words containing negative emotions and emotion information training of which the evaluation information is marked;
the evaluation information comprises user name information for issuing the evaluation information;
the song evaluation module comprises:
the data cleaning sub-module is used for performing data cleaning operation on the evaluation information of the target song when the evaluation information of the target song comprises a plurality of pieces of evaluation information corresponding to the same user name information, reserving one of the evaluation information and removing other evaluation information from the evaluation information;
and the evaluation submodule is used for determining whether the target song is good or not based on the evaluation information left after the data cleaning operation and a preset song evaluation model.
3. A server, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor, when executing the executable instructions, may perform the method of claim 1 above.
4. A computer-readable storage medium comprising instructions which, when executed on the computer, cause the computer to perform the method of claim 1.
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| CN112559794A (en) * | 2019-09-25 | 2021-03-26 | 北京达佳互联信息技术有限公司 | Song quality identification method, device, equipment and storage medium |
| CN111651662B (en) * | 2020-04-15 | 2024-02-23 | 车智互联（北京）科技有限公司 | Content generation method, system and computing device |
| CN113010729B (en) * | 2021-04-28 | 2023-08-18 | 杭州网易云音乐科技有限公司 | Information processing method, device, medium and computing equipment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391483A (en) * | 2017-07-13 | 2017-11-24 | 武汉大学 | A kind of comment on commodity data sensibility classification method based on convolutional neural networks |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140089815A1 (en) * | 2012-09-21 | 2014-03-27 | Google Inc. | Sharing Content-Synchronized Ratings |
| CN105183731B (en) * | 2014-06-04 | 2020-01-21 | 腾讯科技（深圳）有限公司 | Recommendation information generation method, device and system |
| CN105335414B (en) * | 2014-08-01 | 2020-06-02 | 小米科技有限责任公司 | Music recommendation method and device and terminal |
| US10346754B2 (en) * | 2014-09-18 | 2019-07-09 | Sounds Like Me Limited | Method and system for psychological evaluation based on music preferences |
| CN104657457B (en) * | 2015-02-06 | 2017-12-26 | 海信集团有限公司 | A kind of user evaluates data processing method, video recommendation method and the device of video |
| CN105447193A (en) * | 2015-12-22 | 2016-03-30 | 中山大学深圳研究院 | Music recommending system based on machine learning and collaborative filtering |
| CN105868334B (en) * | 2016-03-28 | 2020-10-30 | 云南财经大学 | Feature incremental type-based personalized movie recommendation method and system |
| CN105809488B (en) * | 2016-03-29 | 2020-10-27 | 联想(北京)有限公司 | Information processing method and electronic equipment |
| CN106446048B (en) * | 2016-08-31 | 2019-11-19 | 维沃移动通信有限公司 | A kind of song recommendations method and mobile terminal |
| CN107885745B (en) * | 2016-09-29 | 2020-09-08 | 亿览在线网络技术（北京）有限公司 | Song recommendation method and device |
| CN107038609A (en) * | 2017-04-24 | 2017-08-11 | 广州华企联信息科技有限公司 | A kind of Method of Commodity Recommendation and system based on deep learning |
| CN107908701A (en) * | 2017-11-06 | 2018-04-13 | 广东欧珀移动通信有限公司 | Method, apparatus, storage medium and the terminal device that music is recommended |
| CN107862059A (en) * | 2017-11-14 | 2018-03-30 | 维沃移动通信有限公司 | A kind of song recommendations method and mobile terminal |
-
2018
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Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391483A (en) * | 2017-07-13 | 2017-11-24 | 武汉大学 | A kind of comment on commodity data sensibility classification method based on convolutional neural networks |
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