Patent Publication Number: US-2023161826-A1

Title: Systems and methods for processing emojis in a search and recommendation environment

Description:
BACKGROUND OF THE INVENTION 
     The present disclosure is directed to systems and methods for searching for content recommendations, and in particular, for generating emoji-based metadata for content and processing an emoji-based query using the emoji-based metadata. 
     SUMMARY 
     Searching for content on devices has changed dramatically with the introduction of emojis in recent years. Emojis are small digital images used in electronic messages, and they exist in various genres, including facial expressions, common objects, places and types of weather, and animals. The introduction and proliferation of emojis has changed input search queries too. For example, to search for science fiction movies, a user may wish to type “  movies” instead of “sci-fi movies.” For example, to search for romantic comedy shows, a user could input a search query including emojis such as “  shows.” Thus, input search strings may no longer be solely text-based, but rather, could include a combination of text and emojis. A search and recommendations system needs to be able to handle and process such inputs. 
     User-generated content continues to gain interest in recent years. User-generated content may be posted on various online channels where users and/or channel holders upload content. However, user-generated content might not be associated with enriched metadata. Such content, without associated, well-defined, and enriched metadata, creates a challenge for search and recommendations systems when searching for content based on an input query (e.g., a text string like “detective movies”). 
     To address these shortcomings, systems and methods are described herein for a search and recommendations engine that generates emoji-based metadata associated with content and processes an emoji-based query using the emoji-based metadata. 
     Content without enriched metadata may have information about reactions associated with the content. For example, a user-generated video posted on social media has associated reaction data corresponding to emojis and/or user comments. The information about reactions can be used to generate emoji-based metadata that a system uses to search for content and recommend content. A search and recommendations system may identify one or more content items uploaded (i.e., posted) to one or more social platforms. For example, a user posted a video titled “Crying Babies” on several social platforms (e.g., Facebook, Twitter, etc.). The system identifies the video on any one or more of the social platforms. In some embodiments, the system may identify a content item based on an identifier (e.g., a video title) on one or more social platforms. A system as described in the present disclosure includes control circuitry and storage with appropriate circuitry. In some embodiments, the system includes one or more input/output paths with suitable communications circuitry. 
     A system may retrieve information about instances of a reaction to a content item. In some embodiments, the system retrieves a quantity and/or a frequency of instances of a reaction to a content item. For example, users indicated their reactions to a video titled “Crying Babies” by an interaction with a reaction icon. A reaction icon corresponds to an emoji. For example, the video titled “Crying Babies” has an emoji count of 6000 for   and an emoji count of 4000 for  . In another non-limiting example, the video titled “Crying Babies” has an emoji frequency of 20   per second. 
     A system may retrieve a comment associated with a content item. In some embodiments, the comment has been posted on the same social platform as the content item or another social platform but associated with the content item. The system using control circuitry may identify a comment that is associated with a content item based on the one or more social platforms. In response to identifying the comment associated with the content item, the system using control circuitry may retrieve the comment from the corresponding social platform. For example, a user posted a comment about a video titled “Crying Babies” that was uploaded to YouTube. In this example, the user posted the comment on YouTube in a comments section tied to the video. In another example, the user posted the comment on Facebook and associated the comment to the video by including a link of the video uploaded on YouTube. The system may identify the comment on Facebook as associated with “Crying Babies” based on the link and retrieve the comment from Facebook in response. 
     A system may map a comment to an emoji based on a rule. In some embodiments, a comment is automatically mapped to an emoji based on a rule. In some embodiments, the rule is based at least in part on sentiment analysis. The system using control circuitry executes sentiment analysis on the comment and determines an emoji corresponding to a comment based on sentiment analysis. The system generates a mapping from the comment to the emoji. In some embodiments, one or more emojis correspond to a comment based on sentiment analysis. The system determines associated weights with the one or more emojis based on the sentiment analysis and generates a mapping based on the associated weights. For example, a comment associated with the video titled “Crying Babies” is “Ha Ha, I love it.” The system executes sentiment analysis on the comment and determines that the comment corresponds to both   and  . The system determines, based on the sentiment analysis, that the comment corresponds to a weight of 0.4 associated with   and a weight of 0.6 associated with  . The system then generates a mapping from the comment to the emojis based on the associated weights. 
     A system may generate a factor associated with a content item and an emoji. In some embodiments, the system generates a factor based on information about instances of a reaction associated with a content item and based on mapping of a comment associated with a content item to an emoji. In some embodiments, the system generates emoji-based metadata based on the factor and the emoji. In some embodiments, the system may generate a factor associated with a content item and an emoji based on frequency of instances of a reaction associated with a content item. In some embodiments, the system generates a closeness factor to an emoji for approximate matching techniques (e.g., fuzzy matching). For example, if a content item matches  , the system also generates high closeness factors to some emojis (e.g.,  ,  ) and low closeness factors to others (e.g.,  ). In some embodiments, a system may generate match factors associated with an emoji that is associated with an opposite reaction (i.e., a low closeness factor). For example, the system generates a high match factor for   and a low match factor for   at the same time based on reaction data and closeness factors. In some embodiments, the system may generate a statistics-based match factor associated with a content item and an emoji. The statistical match factor is based on information about instances of a reaction associated with a content item. For example, a video titled “Crying Babies” has various associated reaction data (e.g., count, frequency, associated emoji types, distribution, etc.). The system generates statistical data based on the reaction data (e.g., average count, average frequency, variance, skew, etc.). The system generates the emoji match factor based on the statistical data (e.g., based on average frequency). 
     In some embodiments, a content item includes one or more portions of the content item. For example, a video titled “Crying Babies” includes three portions (i.e., scenes in the video). A system may determine quantities of instances of a reaction associated with each portion based on information about instances of a reaction associated with a content item. The system maps one or more emojis to each portion based on a rule as previously described in the present disclosure. The system generates and associates a factor with each portion based on information about instances of a reaction associated with a content item. In some embodiments, the system may determine a genre of a portion of a content item. The system associates a factor with the genre of a portion. For example, the system determines a scene in a video titled “Crying Babies” is associated with  . Accordingly, the system determines the scene is associated with comedy or a related genre and associates a match factor with the scene genre. 
     A system may store a factor associated with a content item and an emoji. In some embodiments, the system stores the factor in a database in association with an identifier of the content item. For example, the system has generated a match factor based on information about reactions to a video titled “Crying Babies”. The system stores the match factor in a data structure associated with the title identifier “Crying Babies”. The match factor may facilitate processing of an emoji-based query. 
     In some embodiments, a system may process a query having one or more emojis (i.e., emoji-based query) using a match factor. The system receives a query including a text portion and an emoji portion. The system searches a content database to identify content items associated with the query based on the text and emoji portions. In some embodiments, the content database may include a mix of content types (e.g., movies, music, images). In some embodiments, the system may search based on the emoji portion (at least in part) by matching emojis associated with a content item. For example, the system receives a query to search for “  movies”. The system searches for content items associated with  ,  , and movies. The system may execute a matching algorithm (e.g., fuzzy matching) between components of the query and metadata associated with the content items (e.g., between   and emoji-based metadata). In this non-limiting example, the system may find movies that are associated with   but not   based on the matching algorithm and determine the movies are not a match for the query. The system finds movies that match both   and   based on the matching algorithm and identifies the content items as a match for the query. 
     In some embodiments, a system retrieves match scores associated with a content item from a database. In some embodiments, the system retrieves an emoji match score based on an emoji portion of a query and a textual match score based on a text portion of a query. In some embodiments, the system may retrieve respective match scores for each content item of several identified content items based on the search query. For example, a user searches for “    movies”. The system has identified content items associated with  ,  , and movies. The system retrieves associated emoji and textual match scores (e.g., from metadata). Based on the associated match scores, the system determines certain content items are a better match for the search query relative to other content items. In some embodiments, the system may rank the content items based on the match scores. For example, a video titled “Crying Babies” has a high match score for  ,  , and movies. However, a video titled “Truck Drivers” has a low match score for   and movies and a high match score for  . Then, the system ranks “Crying Babies” first and “Truck Drivers” second in the search results based on the match score for  . In some embodiments, the system may generate an aggregate score based on associated match scores. The aggregate score is based on emoji match scores and textual match scores. For example, the system generates aggregate scores for “Crying Babies” and “Truck Drivers” based on the respective match scores for  ,  , and movies (e.g., by summing and normalizing the match scores to the same range). Following this non-limiting example, the system ranks “Crying Babies” first and “Truck Drivers” second based on the aggregate scores. 
     In some embodiments, a system generates for display or causes to be displayed representations of the content items. In some embodiments, the system may cause to be displayed the representations on a remote device different from the system. In some embodiments, the system may order the representations of the content items according to the respective aggregate scores. In some embodiments, representations of the content items include portions of the content items. For example, a search query of “  movies” results in the videos “Crying Babies” and “Truck Drivers”. The system displays a thumbnail of the videos as representations for the results. In another non-limiting example, the system displays a short clip from each video to represent the results. 
     In some embodiments, a system translates an emoji portion of an emoji-based query into text. The system retrieves a second textual match score based on the translated emoji portion for a content item. The system generates a second aggregate score for a content item based on the first aggregate score and the second textual match score. In some embodiments, the system may retrieve respective textual match scores for one or more content items based on the translated emoji portion in order to generate respective second aggregate scores for each content item as described herein. In some embodiments, the first aggregate score and translated emoji portion may contribute differently to the second aggregate score. For example, the system translates   as a text string, “comedy”. The system retrieves different match scores for   and “comedy” for a video titled “Truck Drivers” from associated metadata. In this example, the system retrieves a match score for   from emoji-based metadata and a match score for comedy from other metadata. The system may generate a more accurate aggregate match score for “Truck Drivers” after including the translated emoji portion. 
     In some embodiments, an emoji portion of an emoji-based query has more than one emoji. For example, an emoji-based query may include “ ”. The system may retrieve different emoji match scores for each emoji in a query. In some embodiments, the system determines content items have different emoji match scores based on weighing each emoji of the query differently. In some embodiments, the system displays representations of content items matching a query in a different order based on weighing each emoji differently. The system may generate for display a first representation of a content item based on a first emoji having higher weight in the query. The system may then generate for display a second representation of the content item based on a second emoji having higher weight in the query. Additionally or alternatively, the system may include icons of emojis in the representations of a content item resulting from an emoji-based query. The icons of emojis are ordered based on how each emoji is weighed when the system generates an aggregate score. 
     In some embodiments, a system searches a database to identify portions of a content item associated with a query. Additionally or alternatively, the system may also include genres when searching a database. The system may have determined and stored genres of content items or portions of content items using the techniques described in the present disclosure. In some embodiments, the genres have been associated with emojis in emoji-based metadata. For example, the system may store an associated genre of comedy in metadata associated with a scene in “Crying Babies”. The system associates the scene in “Crying Babies” with comedy based on having reaction data including 5000  . In this non-limiting example, the system has received a query for “  scenes”. The system searches a database and identifies the scene in “Crying Babies” as a match for “  scenes” based on comedy being associated with the scene. 
     In some embodiments, a system matches one or more emojis in a query with a content item based on statistics-based match factors stored in emoji-based metadata. In some embodiments, the system calculates an emoji match score to a content item based on quantity and/or frequency of an emoji associated with a content item. An emoji match may be calculated using various search techniques (e.g., fuzzy matching, statistical closeness, etc.). For example, the system searches for content that matches  . A content item has associated emoji metadata that includes a high match factor for   and a high closeness factor between   and  . Then, the system determines the content item is a good match for  , even though the content item is not directly associated with  . In some embodiments, one or more systems may substantially perform the methods described herein and display results on a remote screen (e.g., a second screen device). 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: 
         FIG.  1    shows an illustrative example of a system generating emoji-based metadata based on information about instances of reactions to content, in accordance with some embodiments of the disclosure; 
         FIG.  2    shows an illustrative block diagram of a system processing an emoji-based query using emoji-based metadata to identify and display content associated with the query, in accordance with some embodiments of the disclosure; 
         FIG.  3    shows an illustrative block diagram of a system for searching for content and providing content recommendations to a computing device, in accordance with some embodiments of the disclosure; 
         FIG.  4    shows an illustrative block diagram of a system generating emoji-based metadata for content based on information about reactions and comments associated with the content, in accordance with some embodiments of the disclosure; 
         FIG.  5    shows an illustrative block diagram of a system generating emoji-based metadata for portions of content items based on information about reactions and comments associated with the portions of content items, in accordance with some embodiments of the disclosure. 
         FIG.  6    shows an illustrative block diagram of a system mapping comment data to emojis, in accordance with some embodiments of the disclosure; 
         FIG.  7    shows an illustrative block diagram of a system processing an emoji-based search query for content, in accordance with some embodiments of the disclosure; 
         FIG.  8    shows an illustrative block diagram of a system for generating aggregate scores based on an untranslated emoji-based search query and a translated emoji-based search query, in accordance with some embodiments of the disclosure; 
         FIG.  9    shows an illustrative block diagram of a system for displaying emoji-based search results, in accordance with some embodiments of the disclosure; 
         FIG.  10    shows a flowchart of a process for generating emoji-based metadata associated with content, in accordance with some embodiments of the disclosure; 
         FIG.  11    shows a flowchart of a process for mapping emojis to portions of content items to include in emoji-based metadata, in accordance with some embodiments of the disclosure; 
         FIG.  12    shows a flowchart of a process for processing an emoji-based search query for content, in accordance with some embodiments of the disclosure. 
         FIG.  13    shows a flowchart of a process for generating aggregate scores based on an untranslated emoji-based search query and a translated emoji-based search query, in accordance with some embodiments of the disclosure; 
     
    
    
     DETAILED DESCRIPTION 
     Systems and methods are described herein for a search and recommendations engine that generates emoji-based metadata associated with content and that processes emoji-based queries using the emoji-based metadata. 
     As referred to herein, the term “content” should be understood to mean an electronically consumable asset accessed using any suitable electronic platform, such as broadcast television programming, pay-per-view programs, on-demand programs (as in video-on-demand (VOD) systems), Internet content (e.g., streaming content, downloadable content, Webcasts, etc.), video clips, audio, information about content, images, animations, documents, playlists, websites and webpages, articles, books, electronic books, blogs, chat sessions, social media, software applications, games, virtual reality media, augmented reality media, and/or any other media or multimedia and/or any combination thereof. 
       FIG.  1    shows an illustrative scenario of a system generating emoji-based metadata based on information about instances of reactions to content, in accordance with some embodiments of the disclosure. System  100  retrieves content item  102  using I/O path with appropriate communications circuitry. Content item  102  includes emoji icons  104  corresponding to various reactions. One or more users interact with the emoji icons (e.g., via touch interaction  106 ) to indicate a reaction to content item  102 . One or more users  108  may also post comments  110  about their reaction to content item  102 . Control circuitry (e.g., from system  100 ) maps comments  110  to various emojis  112 . Reaction data including interactions (e.g., touch interaction  106 ) with the emoji icons and mapped emojis  112  are used by control circuitry (e.g., from system  100 ) to generate emoji-based metadata  114 . 
     Content without enriched metadata may have information about reactions associated with the content. For example, a user-generated video  102  posted on social media has associated reaction data corresponding to emoji icons and/or user comments. The information about reactions can be used to generate emoji-based metadata that a system uses to search for content and recommend content. A search and recommendations system may identify one or more content items uploaded (i.e., posted) to one or more social platforms. For example, a user posted a video titled “Crying Babies” on several social platforms (e.g., Facebook, Twitter, etc.). A system identifies the video on any one or more of the social platforms. In some embodiments, the control circuitry of a system may identify a content item based on an identifier (e.g., a video title) on one or more social platforms. 
     A system may retrieve information about instances of a reaction to a content item. In some embodiments, the control circuitry retrieves a quantity and/or a frequency of instances of a reaction to a content item. For example, users indicated their reactions to a video titled “Crying Babies” by interaction  106  with a reaction icon  104 . A reaction icon may correspond to an emoji. For example, the video titled “Crying Babies” has an emoji count of 6000 for   and an emoji count of 4000 for  . In another non-limiting example, the video titled “Crying Babies” has an emoji frequency of 20   per second. 
     A system may retrieve a comment associated with a content item (e.g., via one or more I/O paths). In some embodiments, the comment has been posted on the same social platform as the content item or another social platform but associated with the content item. The control circuitry may identify a comment that is associated with a content item on the one or more social platforms. In response to identifying that the comment is associated with the content item, the system may retrieve the comment from the corresponding social platform. For example, a user posted a comment about a video titled “Crying Babies” that was uploaded to YouTube. In this example, the user posted the comment on YouTube in a comments section tied to the video. In another example, the user posted the comment on Facebook and associated the comment with the video by including a link of the video uploaded on YouTube. The system may identify the comment on Facebook as associated with “Crying Babies” based on the link and retrieve the comment from Facebook in response. 
     A system may map a comment to an emoji based on a rule. In some embodiments, a comment is automatically mapped to an emoji based on a rule. In some embodiments, the rule is based at least in part on sentiment analysis. Control circuitry (e.g., in system  100 ) may execute sentiment analysis on the comment and determine an emoji corresponding to a comment based on sentiment analysis. The control circuitry generates a mapping from the comment to the emoji. In some embodiments, one or more emojis correspond to a comment based on sentiment analysis. The control circuitry determines associated weights with the one or more emojis based on the sentiment analysis and generates a mapping based on the associated weights. For example, comment  110  associated with the video titled “Crying Babies” is “Ha Ha, I love it.” 
     System  100  executes sentiment analysis on the comment and determines that the comment corresponds to emojis  112  (i.e.,   and  ). System  100  determines, based on the sentiment analysis, that the comment corresponds to a weight of 0.4 associated with   and a weight of 0.6 associated with  . System  100  then generates a mapping from the comment to the emojis based on the associated weights. 
       FIG.  2    shows an illustrative scenario of a system processing an emoji-based query using emoji-based metadata to identify and display content associated with the query, in accordance with some embodiments of the disclosure. System  200  receives an emoji-based search query  202 . Search and recommendations engine  204  may process search query  202 . Engine  204  may identify content items  206  based on query  202  and emoji-based and/or other metadata (e.g., emoji metadata  208  and other metadata  210 ). Engine  204  may generate and cause to be displayed search results  212  and  214 . Search results  212  and  214  are ordered based on weight of emojis  216  and  218 , where emojis  216  and  218  are from emoji-based query  202 . 
     In some embodiments, a system may process a query having one or more emojis (i.e., emoji-based query) using a match factor. Control circuitry (e.g., in system  200 ) receives a query including a text portion and an emoji portion. The control circuitry searches a content database to identify content items associated with the query based on the text and untranslated emoji portions. In some embodiments, the content database may include a mix of content types (e.g., movies, music, images). In some embodiments, the control circuitry may search based on the untranslated emoji portion (at least in part) by matching emojis associated with a content item. For example, system  200  receives query  202  to search for “    movies”. System  200  searches for content items  206  associated with  ,  , and movies based on metadata  208  and  210 . System  200  may execute a matching algorithm (e.g., fuzzy matching) between components of the query and metadata associated with the content items (e.g., between   and emoji-based metadata). System  200  may find movies that are associated with   but not   based on the matching algorithm and determine the movies are not a match for the query. The system finds movies that match both   and   based on the matching algorithm and identifies the content items as a match for the query. 
     In some embodiments, a system retrieves match scores associated with a content item from a database. In such embodiments, control circuitry (e.g. in system  200 ) retrieves an emoji match score based on an emoji portion of a query and a textual match score based on a text portion of a query. In some embodiments, the control circuitry may retrieve respective match scores for each content item of several identified content items based on the search query. For example, a user searches for “  movies”. System  200  has identified content items associated with  ,  , and movies. System  200  retrieves associated emoji and textual match scores (e.g., from metadata). Based on the associated match scores, system  200  determines certain content items are a better match for the search query relative to other content items. In some embodiments, the system may rank the content items based on the match scores. For example, a video titled “Crying Babies” has a high match score for  ,  , and movies. However, a video titled “Truck Drivers” has a low match score for   and movies and a high match score for  . Then, a system ranks “Crying Babies” first and “Truck Drivers” second in the search results based on the match score for  . In some embodiments, the control circuitry (e.g., in system  200 ) may generate an aggregate score based on associated match scores. The aggregate score is based at least in part on emoji match scores and textual match scores. For example, system  200  generates aggregate scores for “Crying Babies” and “Truck Drivers” based on the respective match scores for  ,  , and movies (e.g., by summing and normalizing the match scores to the same range). Following this non-limiting example, system  200  ranks “Crying Babies” first and “Truck Drivers” second based on the aggregate scores. 
       FIG.  3    shows an illustrative block diagram of a system  300  for searching for content and providing content recommendations to a computing device, in accordance with some embodiments of the disclosure. System  300  may, in some embodiments, further represent system  100  and/or system  200 . Although  FIG.  3    shows system  300  as including a number and configuration of individual components, in some embodiments, any number of the components of system  300  may be combined and/or integrated as one device. System  300  includes recommendations engine  301 , which may be a server, as well as communications network  321 , computing device  331 , media content source  322 , and search engine data source  324 . Recommendations engine  301  is communicatively coupled to computing device  331 , media content source  322 , and search engine data source  324  by way of communications network  321 , which may include the Internet and/or any other suitable wired and/or wireless communications paths, networks and/or groups of networks. Recommendations engine  301  may include a search engine and/or be able to substantially perform the functions of a search and recommendations engine as described in the present disclosure. 
     In some embodiments, system  300  excludes recommendations engine  301 , and functionality that would otherwise be implemented by recommendations engine  301  is instead implemented by other components of system  300 , such as computing device  331 . In other embodiments, recommendations engine  301  works in conjunction with computing device  331  to implement certain functionality described herein in a distributed or cooperative manner. 
     Recommendations engine  301  includes control circuitry  302  and input/output (hereinafter “I/O”) path  308 , and control circuitry  302  includes storage  306  and processing circuitry  304 . Computing device  331  includes control circuitry  332 , I/O path  338 , speaker  340 , display  342 , and user input interface  344 . Control circuitry  332  includes storage  336  and processing circuitry  334 . Control circuitry  302  and/or  332  may be based on any suitable processing circuitry such as processing circuitry  304  and/or  334 . As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores). In some embodiments, processing circuitry may be distributed across multiple separate processors, for example, multiple of the same type of processors (e.g., two Intel Core i9 processors) or multiple different processors (e.g., an Intel Core i7 processor and an Intel Core i9 processor). 
     Each of storage  306 , storage  336 , and/or storages of other components of system  300  (e.g., storages of media content source  322 , search engine data source  324 , and/or the like) may be an electronic storage device. As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as random-access memory, read-only memory, hard drives, optical drives, digital video disc (DVD) recorders, compact disc (CD) recorders, BLU-RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders (DVRs, sometimes called personal video recorders, or PVRs), solid state devices, quantum storage devices, gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same. Each of storage  306 , storage  336 , and/or storages of other components of system  300  may be used to store various types of content, metadata, and or other types of data. Non-volatile memory may also be used (e.g., to launch a boot-up routine and other instructions). Cloud-based storage may be used to supplement storages  306 ,  336  or instead of storages  306 ,  336 . In some embodiments, control circuitry  302  and/or  332  executes instructions for an application stored in memory (e.g., storage  306  and/or  336 ). Specifically, control circuitry  302  and/or  332  may be instructed by the application to perform the functions discussed herein. In some implementations, any action performed by control circuitry  302  and/or  332  may be based on instructions received from the application. For example, the application may be implemented as software or a set of executable instructions that may be stored in storage  306  and/or  336  and executed by control circuitry  302  and/or  332 . In some embodiments, the application may be a client/server application where only a client application resides on computing device  331  and a server application resides on recommendations engine  301 . 
     The application may be implemented using any suitable architecture. For example, it may be a stand-alone application wholly implemented on computing device  331 . In such an approach, instructions for the application are stored locally (e.g., in storage  336 ), and data for use by the application is downloaded on a periodic basis (e.g., from an out-of-band feed, from an Internet resource, or using another suitable approach). Control circuitry  332  may retrieve instructions for the application from storage  336  and process the instructions to perform the functionality described herein. Based on the processed instructions, control circuitry  332  may determine what action to perform when input is received from user input interface  344 . 
     In client/server-based embodiments, control circuitry  332  may include communications circuitry suitable for communicating with an application server (e.g., recommendations engine  301 ) or other networks or servers. The instructions for carrying out the functionality described herein may be stored on the application server. Communications circuitry may include a cable modem, an Ethernet card, or a wireless modem for communication with other equipment, or any other suitable communications circuitry. Such communications may involve the Internet or any other suitable communications networks or paths (e.g., communications network  321 ). In another example of a client/server-based application, control circuitry  332  runs a web browser that interprets web pages provided by a remote server (e.g., recommendations engine  301 ). For example, the remote server may store the instructions for the application in a storage device. The remote server may process the stored instructions using circuitry (e.g., control circuitry  302 ) and/or generate displays. Computing device  331  may receive the displays generated by the remote server and may display the content of the displays locally via display  342 . This way, the processing of the instructions is performed remotely (e.g., by recommendations engine  301 ) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device  331 . Computing device  331  may receive inputs from the user via input interface  344  and transmit those inputs to the remote server for processing and generating the corresponding displays. 
     A user may send instructions to control circuitry  302  and/or  332  using user input interface  344 . User input interface  344  may be any suitable user interface, such as a remote control, trackball, keypad, keyboard, touchscreen, touchpad, stylus, joystick, voice recognition interface, gaming controller, or other user input interfaces. User input interface  344  may be integrated with or combined with display  342 , respectively, which may be a monitor, a television, a liquid crystal display (LCD), an electronic ink display, or any other equipment suitable for displaying visual images. 
     Recommendations engine  301  and computing devices  331  may transmit and receive content and data via one or more of I/O paths  308  and  338 . I/O paths  308  and  338  may be or include appropriate communications circuitry. For instance, I/O path  308  and/or I/O path  338  may include a communications port configured to transmit and/or receive (for instance to and/or from media content source  322  and/or search engine data source  324 ), via communications network  321 , content item identifiers, natural language queries, and/or other data. Control circuitry  302 ,  332  may be used to send and receive commands, requests, and other suitable data using I/O paths  308 ,  338 . 
       FIG.  4    shows an illustrative block diagram of a system generating emoji-based metadata for content based on information about reactions and comments associated with the content, in accordance with some embodiments of the disclosure. System  400  retrieves content item  402  (e.g., using communications network  321  from content source  322 ). Content item  402  includes comment data  404  and reaction data  410 . Comment data  404  is processed by mapping engine  406  (e.g., using control circuitry). Mapping engine  406  maps comments in comment data  404  to various emojis to indicate associated reactions. Mapping engine  406  generates emoji mapping  408  based on the comments. Search and recommendations engine  412  receives emoji mapping  408  and reaction data  410 . Reaction data  410  includes interactions with emoji icons to indicate reactions to content item  402 . Search and recommendations engine  412  generates (e.g., using control circuitry  302 ) emoji-based metadata  414  based on reaction data  410  and emoji mapping  408 . 
     A system may generate a factor (e.g., using control circuitry  302 ) associated with a content item and an emoji. In some embodiments, the control circuitry generates a factor based on information about instances of a reaction associated with a content item and based on mapping of a comment associated with a content item to an emoji. In some embodiments, the control circuitry generates emoji-based metadata based on the factor and the emoji. The control circuitry may generate a factor associated with a content item and an emoji based on frequency of instances of a reaction associated with a content item. In some embodiments, the control circuitry generates a closeness factor to an emoji for approximate matching techniques (e.g., fuzzy matching). For example, if a content item matches  , system  400  also generates (e.g., by way of control circuitry  302 ) high closeness factors to some emojis (e.g.,  ,  ) and low closeness factors to others (e.g.,  ). In some embodiments, the control circuitry may generate match factors associated with an emoji that is associated with an opposite reaction (i.e., a low closeness factor). For example, system  400  generates a high match factor for   and a low match factor for   at the same time based on reaction data and closeness factors. In some embodiments, the control circuitry may generate a statistics-based match factor associated with a content item and an emoji. The statistical match factor is based on information about instances of a reaction associated with a content item. For example, a video titled “Crying Babies” has various associated reaction data (e.g., count, frequency, associated emoji types, distribution, etc.). System  400  generates statistical data based on the reaction data (e.g., average count, average frequency, variance, skew, etc.). System  400  generates the emoji match factor based on the statistical data (e.g., based on average frequency). 
     A system may store a factor associated with a content item and an emoji (e.g., in storage  306  using control circuitry  302 ). In some embodiments, the control circuitry stores the factor in a database in association with an identifier of the content item. For example, a system has generated a match factor based on information about reactions to a video titled “Crying Babies”. The system stores the match factor in a data structure associated with the title identifier “Crying Babies”. The match factor may facilitate processing of an emoji-based query. 
       FIG.  5    shows an illustrative block diagram of a system generating emoji-based metadata for portions of content items based on information about reactions and comments associated with the portions of content items, in accordance with some embodiments of the disclosure. System  500  receives content item  502  including portion  504  (e.g., via communications network  321 ). System  500  receives portion comment data  506  associated with portion  504 . Emoji mapping generator  510  uses portion comment data  506  to map the comments to various emojis. Control circuitry (e.g., control circuitry  302 ) combines the emoji mappings from mapping generator  510  with portion reaction data  508  associated with portion  504  into statistical data  512 . Statistical data  512  includes emoji count data  514  and emoji frequency data  516 . Search and recommendations engine  518  receives and processes statistical data  512  to generate emoji-based metadata  520  about portion  504  (e.g., using control circuitry  302 ). Metadata  520  includes emoji metadata  522  and associated content genres  524  based on statistical data  512 . 
     A content item may include one or more portions. For example, a video titled “Crying Babies” includes three portions (i.e., scenes in the video). Control circuitry (e.g, from system  500 ) may determine quantities of instances of a reaction associated with each portion based on information about instances of a reaction associated with a content item. The control circuitry maps one or more emojis to each portion based on a rule as previously described in the present disclosure. The control circuitry generates and associates a factor with each portion based on information about instances of a reaction associated with a content item. In some embodiments, the control circuitry may determine a genre of a portion of a content item. The control circuitry associates a factor with the genre of a portion. For example, system  500  determines a scene in a video titled “Crying Babies” is associated with  . Accordingly, system  500  determines the scene is associated with comedy or a related genre and associates a match factor with the scene genre. 
       FIG.  6    shows an illustrative block diagram of a system mapping comment data to emojis, in accordance with some embodiments of the disclosure. System  600  may be a mapping engine (e.g., mapping engine  406 ). System  600  receives comment data  602  associated with one or more content items. Sentiment analyzer  604  processes comment data  602  using sentiment analysis and provides the analysis results to emoji mapping generator  606 . Emoji mapping generator  606 , based on the results of the sentiment analysis, generates mappings between comment data  602  and one or more emojis. Emoji group  608  provides some example emojis, which may represent reactions based on comment data  602 . 
       FIG.  7    shows an illustrative block diagram of a system processing an emoji-based search query for content, in accordance with some embodiments of the disclosure. System  700  receives emoji-based search query  702 , which includes an emoji portion and a textual portion. Search and recommendations engine  704  receives (e.g., using I/O path  308  and appropriate circuitry) and processes emoji-based search query  702  (e.g., using control circuitry  302 ). The control circuitry from search and recommendations engine  704  searches a content database (e.g., content source  322 ) and identifies content items  706  matching query  702  based on emoji-based metadata and/or other metadata (e.g., emoji metadata  708  and other metadata  710 ). The control circuitry retrieves emoji match scores  712  based on the emoji portion and other match scores  714  based on the textual portion (e.g., from metadata  708  and  710 ). Score aggregator  716  receives scores  712  and  714  and generates respective aggregate scores for the identified content items  706 . System  700  generates search results data  718 , where the search results are ranked based on the aggregate scores. System  700  may display search results data  718  or cause search results data  718  to be displayed on another device (e.g., on computing device  331 ). 
     In some embodiments, a system searches a database to identify portions of a content item associated with a query. Additionally or alternatively, the system may also include genres when searching a database. A system may have determined and stored genres of content items or portions of content items using the techniques described in the present disclosure. In some embodiments, the genres have been associated with emojis in emoji-based metadata. For example, system  500  stored an associated genre of comedy for “Crying Babies” in metadata. System  500  associated the scene in “Crying Babies” with comedy based on the reaction data (e.g., an emoji count of 5000  )). System  700  receives a query for “  scenes”. System  700  searches a database using a search engine and identifies, using control circuitry, the scene in “Crying Babies” as a match for “  scenes” based on comedy being associated with the scene. 
     In some embodiments, control circuitry in a system matches one or more emojis in a query with a content item based on statistics-based match factors stored in emoji-based metadata. In some embodiments, the control circuitry calculates an emoji match score to a content item based on quantity and/or frequency of an emoji associated with a content item. An emoji match may be calculated using various search techniques (e.g., fuzzy matching, statistical closeness, etc.). For example, system  700  searches for content that matches   using a search engine. A content item has associated emoji metadata that includes a high match factor for   and a high closeness factor between   and  . Then, system  700  determines the content item is a good match for  , even though the content item is not directly associated with  . In some embodiments, one or more systems may substantially perform the methods described herein and display results on a remote screen (e.g., display  342  on computing device  331  using communications network  321 ). In some embodiments, an aggregate match score may be calculated based on an emoji-based metadata match factor and a textual match factor (e.g., using control circuitry  302 ). The match factors may contribute differently to the match score. As a non-limiting example, a search query may be “  programs”. An emoji match factor for   is denoted “E” and a match factor including all other match factors (e.g., textual match factor for programs) is denoted “O”. The weights of each match factor may be denoted “a” and “b”, respectively. In this example, the aggregate match score “P” is calculated by 
     
       
      
       P=a*E+b*O.  
      
     
     Further to this example, an emoji portion is also translated into a text string (e.g., comedy). The translated emoji match factor for comedy is denoted “T” with associated weight denoted “c”. In this example, the weights “a”, “b”, and “c” sum to one. Then, a second aggregate match score “S” may be calculated by 
     
       
      
       S=P+c*T=a*E+b*O+c*T.  
      
     
       FIG.  8    shows an illustrative block diagram of a system for generating aggregate scores based on an untranslated emoji-based search query and a translated emoji-based search query, in accordance with some embodiments of the disclosure. System  800  includes search and recommendations engine  802  and emoji translator  806 . Emoji translator  806  receives emoji portion  804  of a search query (e.g., search query  702 ). Emoji translator  806  translates (e.g., using control circuitry  302 ) the emoji portion into a text string for processing by search and recommendations engine  802 . Engine  802  retrieves translated emoji match score  808  for the translated emoji portion (e.g., from other metadata  710 ). Score aggregator  810  generates aggregate scores based on emoji match scores  812 , other match scores  814 , and translated emoji match scores  808 . Previous aggregate scores based on emoji match scores  812  and other match scores  814  may have been generated (e.g., by system  700 ). Score aggregator  810  may then generate aggregate scores based on the previous aggregate scores and translated emoji match scores  808 . System  800  generates search results data  816 , which are ordered based on the aggregate scores. Search results data  816  may be used to generate for display representations of the search results (e.g., by system  900  as described in  FIG.  9   ). 
     In some embodiments, a system translates an emoji portion of an emoji-based query into text. Control circuitry from the system retrieves a second textual match score based on the translated emoji portion for a content item. The control circuitry generates a second aggregate score for a content item based on the first aggregate score and the second textual match score. In some embodiments, the control circuitry may retrieve respective textual match scores for one or more content items based on the translated emoji portion in order to generate respective second aggregate scores for each content item as described herein. In some embodiments, the first aggregate score and translated emoji portion may contribute differently to the second aggregate score. For example, system  800  translates   as a text string, “comedy”. System  800  retrieves different match scores for   and “comedy” for a video titled “Truck Drivers” from associated metadata. System  800  retrieves a match score for   from emoji-based metadata and a match score for comedy from other metadata. System  800  may generate a more accurate aggregate match score for “Truck Drivers” after including the translated emoji portion. 
       FIG.  9    shows an illustrative block diagram of a system for displaying emoji-based search results, in accordance with some embodiments of the disclosure. System  900  receives search results data  902 . For example, search results data  902  may be search results data  718  and/or search results data  816 . Display generator  904  generates for display (e.g., using display circuitry as instructed by control circuitry  302 ) representations of the search results based on search results data  902 . Search results data  902  may include a ranking of the search results based on the aggregate scores (e.g., as described in relation to  FIGS.  7  and  8   ). Display generator  904  generates for display the representations  906  and  908  (e.g., a short clip for each video result). Display generator  904  may generate emoji icons  910  and  912 , which may indicate how each emoji was weighed when searching for the content items. For example, emoji icons  910  indicate   had higher weight than   in result  906 . For example, emoji icons  912  indicate   had higher weight than   in result  908 . 
     In some embodiments, a system generates for display or causes to be displayed representations of the content items. In some embodiments, control circuitry from the system may cause to be displayed the representations on a remote device different from the system (e.g., on display  342 ). In some embodiments, the control circuitry may order the representations of the content items according to the respective aggregate scores. In some embodiments, representations of the content items include portions of the content items. For example, a search query of “  movies” results in the videos “Crying Babies” and “Truck Drivers”. System  900  displays thumbnails of search results  906  and  908  as representations for the results. In another non-limiting example, system  900  displays short clips of search results  906  and  908  to represent the results. 
     In some embodiments, an emoji portion of an emoji-based query has more than one emoji. For example, an emoji-based query includes “ ”. A system may retrieve different emoji match scores for each emoji in a query. In some embodiments, control circuitry in the system determines content items have different emoji match scores based on weighing each emoji of the query differently. In some embodiments, a system displays representations of content items matching a query in a different order based on weighing each emoji differently. Additionally or alternatively, the system may display icons of emojis in the representations of a content item resulting from an emoji-based query. For example, system  900  may generate for display search result  906  with emoji icons  910  in which   had higher weight. System  900  may then generate for display search result  908  with emoji icons  912  in which   had higher weight. 
       FIG.  10    shows a flowchart of a process  1000  for generating emoji-based metadata associated with content, in accordance with some embodiments of the disclosure. Process  1000 , and any of the following processes, may be executed by control circuitry (e.g., control circuitry  302 ). The control circuitry may be part of a recommendations engine or a search engine or may be part of a remote server separate from the recommendations engine by way of a communications network or distributed over a combination of both. A system (e.g., system  200 ) may perform process  1000  as described herein. At  1002 , a content item that was posted to a social platform is identified. The content item may have been posted to more than one social platform. Control circuitry in a system (e.g., system  400 ) may identify the content item based on any one of the social platforms. At  1004 , a quantity of instances of a reaction to the content item is retrieved. The reaction corresponds to an emoji. The system may retrieve the quantity of instances (i.e., an emoji count) using control circuitry and/or communications circuitry (e.g., control circuitry  302  and/or I/O path  308 ). At  1006 , a comment is retrieved. The comment is posted in association with the content item via the social platform. In some embodiments, the comment may have been posted on a different social platform from the social platform where the content item was posted, but the comment is associated with the content item (e.g., by including a hyperlink to the content item). Control circuitry may retrieve the comment in response to determining the comment is associated with the content item. At  1008 , the comment is mapped to the emoji based on a rule. The rule may be based at least in part on sentiment analysis. Control circuitry may execute the sentiment analysis and generate a mapping between the comment and the emoji based on the sentiment analysis. At  1010 , a factor is generated. The factor is associated with the content item and the emoji based on the quantity of instances of the reaction and based on the mapping between the comment and the emoji. At  1012 , the factor is stored in a database in association with an identifier of the content item (e.g., in a data structure associated with a title of the content item). The factor may be stored in emoji-based metadata  1014 . The stored factor may be used by a system (e.g., system  700 ) to facilitate processing of an emoji-based query. 
       FIG.  11    shows a flowchart of a process for mapping emojis to portions of content to include in emoji-based metadata, in accordance with some embodiments of the disclosure. Process  1100  may be performed by a system (e.g., system  500 ) in addition to (or as part of) process  1000 . At  1102 , a second quantity of instances of a reaction is determined (e.g., using control circuitry  302 ) based on portion comment data  1104  and portion reaction data  1106 . The second quantity is associated with a portion of the content item. At  1108 , the emoji is mapped to the portion (e.g., by system  600 ) based on the rule (e.g., sentiment analysis). At  1110 , the factor is associated with the portion based on the second quantity of instances of the reaction prior to storing the factor in a database. For example, system  400  generates the factor and associates the factor with the portion before storing the factor in a database including emoji-based metadata  1112 . 
       FIG.  12    shows a flowchart of a process for processing an emoji-based search query for content, in accordance with some embodiments of the disclosure. At  1202 , an emoji-based search query is received. The query includes a text portion and an emoji portion. At  1204 , a system searches (e.g., using control circuitry  302 ) a database (e.g., search engine data source  324  and/or content source  322 ) for content items associated with the query (e.g., based on emoji metadata  1206  and other metadata  1208 ). The control circuitry may search for content items based at least in part on matching emojis from the emoji portion to the content items (e.g., by matching emojis to associated emoji metadata  1206  of content items using control circuitry  302 ). At  1210 , match scores are retrieved for each content item identified from searching the database. The match scores include an emoji match score based on the emoji portion and a textual match score based on the text portion of the query. At  1212 , an aggregate score for each identified content item is generated based on the respective emoji match score and textual match score. For example, system  700  generates aggregate scores using score aggregator  716 . The respective aggregate score may be a first aggregate score. 
       FIG.  13    shows a flowchart of a process for generating aggregate scores based on an untranslated emoji-based search query and a translated emoji-based search query, in accordance with some embodiments of the disclosure. At  1302 , an emoji portion of a search query (e.g., emoji portion  1304 ) may be translated into a text string (e.g., using control circuitry  302 ). At  1306 , a second textual match score is retrieved for each content item based on the translated emoji portion (e.g., using I/O path  308  and appropriate circuitry). At  1308 , a second aggregate score is generated for each content item based on the respective aggregate score and second textual match score. For example, system  800  translates emoji portion  804  using control circuitry  302  and retrieves translated emoji match score  808  via I/O path  308  using appropriate circuitry. Score aggregator  810  generates the second aggregate score using control circuitry  302  for search results  816  based on emoji match scores  812 , other match scores  814 , and translated match scores  808 . 
     As referred to herein, the term “in response to” refers to initiated as a result of. For example, a first action being performed in response to a second action may include interstitial steps between the first action and the second action. As referred to herein, the term “directly in response to” refers to caused by. For example, a first action being performed directly in response to a second action may not include interstitial steps between the first action and the second action. 
     The systems and processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the actions of the processes discussed herein may be omitted, modified, combined, and/or rearranged, and any additional actions may be performed without departing from the scope of the invention. More generally, the above disclosure is meant to be exemplary and not limiting. Only the claims that follow are meant to set bounds as to what the present disclosure includes. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.