WO2016137507A1

WO2016137507A1 - Visualization of user review data

Info

Publication number: WO2016137507A1
Application number: PCT/US2015/018088
Authority: WO
Inventors: John Dillon EVERSMAN
Original assignee: Hewlett Packard Enterprise Development Lp
Priority date: 2015-02-27
Filing date: 2015-02-27
Publication date: 2016-09-01

Abstract

Examples disclosed herein relate to visualization of user review data. The examples enable obtaining a set of user reviews associated with an item from at least one source. A user review of the set of user reviews may comprise at least one of: a review rating, review content, identification information of a user who created the user review, and information related to quality of the user review. The examples enable generating a bar graph comprising a first bar composed of a first set of cells that represent a first subset of user reviews of the set of user reviews by arranging the first set of cells in the order of the quality of individual user reviews of the first subset of user reviews.

Description

VISUALIZATION OF USER REVIEW DATA

BACKGROUND

[0001] User review data may allow a potential purchaser of an item to evaluate what other users or customers think about the item before making a decision on the purchase. The user review data may include a review rating of the item as well as review content (e.g., free-form comments regarding the user's impression of the item and its features, experience with the item, overall satisfaction with the item, and the like).

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] The following detailed description references the drawings, wherein:

[0003] FIG. 1 is a block diagram depicting an example environment in which various examples may be implemented as a user review visualization system.

[0004] FIG. 2 is a block diagram depicting an example user review visualization system.

[0005] FIG. 3 is a block diagram depicting an example machine-readable storage medium comprising instructions executable by a processor for visualization of user review data.

[0006] FIG. 4 is a block diagram depicting an example machine-readable storage medium comprising instructions executable by a processor for visualization of user review data.

[0007] FIG. 5 is a flow diagram depicting an example method for visualization of user review data. [0008] FIG. 6 is a flow diagram depicting an example method for visualization of user review data.

[0009] FIG. 7 is a diagram depicting an example user interface for visualization of user review data.

[0010] FIG. 8 is a diagram depicting an example user interface for visualization of user review data.

[0011] FIG. 9 is a diagram depicting an example user interface for visualization of user review data.

DETAILED DESCRIPTION

[0012] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only. While several examples are described in this document, modifications, adaptations, and other implementations are possible. Accordingly, the following detailed description does not limit the disclosed examples. Instead, the proper scope of the disclosed examples may be defined by the appended claims.

[0013] User review data may allow a potential purchaser of an item to evaluate what other users or customers think about the item before making a decision on the purchase. The user review data may include a review rating of the item, review content (e.g., free-form comments regarding the user's impression of the item and its features, experience with the item, overall satisfaction with the item, and the like), and/or other information. The user review data may originate from a plurality of sources including an online shopping site, a consumer review site , a social media site , a survey site, and/or any other sources that collect and/or gather user reviews on various items. [0014] With the increased popularity of Internet commerce, there may exist hundreds or thousands of user reviews, covering many varied aspects of the item. However, this vast amount of user review data may present too much information for some customers to digest or even for some manufacturers, suppliers, distributers or other related parties of the item to view and analyze in a meaningful way.

[0015] Examples disclosed herein provide technical solutions to these technical challenges by providing a tool that visualizes various aspects of the user review data. The examples disclosed herein enable obtaining a set of user reviews associated with an item from at least one source. A user review of the set of user reviews may comprise at least one of: a review rating, review content, identification information of a user who created the user review, and information related to quality of the user review. The examples enable generating a bar graph comprising a first bar composed of a first set of cells that represent a first subset of user reviews of the set of user reviews by arranging the first set of cells in the order of the quality of individual user reviews of the first subset of user reviews.

[0016] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term "plurality," as used herein, is defined as two or more than two. The term "another," as used herein, is defined as at least a second or more. The term "coupled," as used herein, is defined as connected, whether directly without any intervening elements or indirectly with at least one intervening elements, unless otherwise indicated. Two elements can be coupled mechanically, electrically, or communicatively linked through a communication channel, pathway, network, or system. The term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will also be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms, as these terms are only used to distinguish one element from another unless stated otherwise or the context indicates otherwise. As used herein, the term "includes" means includes but not limited to, the term "including" means including but not limited to. The term "based on" means based at least in part on.

[0017] FIG. 1 is an example environment 100 in which various examples may be implemented as a user review visualization system 110. Environment 100 may include various components including server computing device 130 and client computing devices 140 (illustrated as 140A, 140B, ..., 140N). Each client computing device 140A, 140B, ..., 140N may communicate requests to and/or receive responses from server computing device 130. Server computing device 130 may receive and/or respond to requests from client computing devices 140. Client computing devices 140 may be any type of computing device providing a user interface through which a user can interact with a software application. For example, client computing devices 140 may include a laptop computing device, a desktop computing device, an all-in-one computing device, a tablet computing device, a mobile phone, an electronic book reader, a network-enabled appliance such as a "Smart" television, and/or other electronic device suitable for displaying a user interface and processing user interactions with the displayed interface. While server computing device 130 is depicted as a single computing device, server computing device 130 may include any number of integrated or distributed computing devices serving at least one software application for consumption by client computing devices 140.

[0018] The various components (e.g., components 129, 130, and/or 140) depicted in FIG. 1 may be coupled to at least one other component via a network 50. Network 50 may comprise any infrastructure or combination of infrastructures that enable electronic communication between the components. For example, network 50 may include at least one of the Internet, an intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a SAN (Storage Area Network), a MAN (Metropolitan Area Network), a wireless network, a cellular communications network, a Public Switched Telephone Network, and/or other network. According to various implementations, user review visualization system 110 and the various components described herein may be implemented in hardware and/or a combination of hardware and programming that configures hardware. Furthermore, in FIG. 1 and other Figures described herein, different numbers of components or entities than depicted may be used.

[0019] User review visualization system 110 may comprise a data obtain engine 121 , a time bar engine 122, a user review engine 123, a keyword engine 124, a bar graph engine 125, and/or other engines. The term "engine", as used herein, refers to a combination of hardware and programming that performs a designated function. As is illustrated respect to FIGS. 3-4, the hardware of each engine, for example, may include one or both of a processor and a machine-readable storage medium, while the programming is instructions or code stored on the machine-readable storage medium and executable by the processor to perform the designated function.

[0020] Data obtain engine 121 may obtain a set of user reviews associated with an item from at least one source. An "item," as used herein, may refer to a product (e.g., a physical product such as a book, machine, etc., a digital product such as a digital media, etc.), a service, a vendor, a venue, and/or any other item or a group of items that a user may create and/or submit a user review for.

[0021] A "user review," as used herein, may comprise at least one of: a review title (e.g., a title provided by the user who created the user review), a review category (e.g., product category), a review rating (e.g., indicating a degree of positiveness or negativeness of the review content), review content, identification (ID) information of the user who created the user review (e.g., user name, user ID, Internet Protocol (IP) address, etc.), a timestamp (e.g., indicating the time that the user review is created, submitted, modified, and/or updated), information related to quality of the user review (e.g., helpfulness or unhelpfulness of the user review), and/or other information that describes or otherwise relates to the user review. The review content, for example, may include a user's feedback and/or comments on a product that the user purchased via online shopping or the user's feedback and/or comments on food, service, location, etc. related to a restaurant that the user visited. The review rating may be, for example, represented by a number of stars (e.g., 3 stars out of 5 stars) or any other positive or negative indicators or a numerical score (e.g., 6 out of 10).

[0022] The "quality" of the user review, as used herein, may be determined based on a degree of reputations associated with the user who created the user review (e.g., the user who frequently provided user reviews for various items in the past may have a higher reputation score than another user who provided the user review for the first time), feedback on the user review given by at least one user other than the user who created the user review (e.g., other users may view the user review and answer to a question that asks whether the user review was helpful or unhelpful), a timestamp associated with the user review (e.g., a more recent user review may indicate a better quality review), a length (or size) of the user review (e.g., a longer user review may indicate a better quality review), and/or other factors related to the reliability, credibility, and/or validity of the user review.

[0023] A "source," as used herein, may include an online shopping site, a consumer review site , a social media site, a survey site, and/or any other sources that collect and/or gather user reviews on various items. In some implementations, data obtain engine 121 may pull the user review data (e.g., any newly added and/or modified user reviews) from at least one source upon a request and/or at a predetermined time interval (e.g., the user review data may be pulled everyday at midnight). In other implementations, the at least one source may publish (or push) the user review data (e.g., any newly added and/or modified user reviews) to system 110 for data obtain engine 121 to obtain the user review data.

[0024] Time bar engine 122 may cause a display of a time slider bar comprising at least one bar control that is used to specify a time period. For example, the time slider bar may be displayed via a user interface on a client computing device (e.g., client computing device 140A) where a user may adjust at least one bar control to specify a time period on the time slider bar. Example user interfaces for displaying the time slider bar are illustrated in FIGS. 7-9. In the examples illustrated in FIGS. 7- 9, the time slider bar (e.g., item 710 of FIG. 7, item 810 of FIG. 8, and item 910 of FIG. 9) includes two bar controls that can be moved along the time slider bar to specify a desired time period. A first bar control of the two bar controls may specify a start time of the time period where a second bar control of the two bar controls may specify an end time of the time period. In one example, the time slider bar may begin at the product release date of an item and end at the most recent date of the data pull of the user review data from the at least one source and/or the most recent date of the publication of the user review data by the at least one source. A user may move the first bar control to specify the start time of the time period (e.g., 2 months after the product release date) and/or the second bar control to specify the end time of the time period (e.g., 8 months after the product release date or 2 months before the most recent date of the data pull or the publication of the user review data).

[0025] In some implementations, by specifying and/or re-specifying the time period by moving the at least one bar control along the time slider bar (e.g., displayed by time bar engine 122), some user reviews of the set of user reviews may be filtered in and/or out based on their respective timestamps. For example, user reviews associated with a timestamp that is outside (or inside) of the specified time period may be excluded from (or included in) the set of user reviews. Accordingly, a display of keywords related to the set of user reviews (e.g., as discussed herein with respect to keyword engine 124) and/or a display of a bar graph (e.g., as discussed herein with respect to bar graph engine 125) may be updated based on which user reviews are included in and/or excluded from the set of user reviews.

[0026] User review engine 123 may generate a summary of the set of user reviews (e.g., obtained by data obtain engine 121) and/or cause a display of the summary. The summary may comprise a total number of user reviews in the set of user reviews, an average, highest, lowest, or median review rating for the set of user reviews, an average, highest, lowest, or median quality for the set of user reviews, etc. Example user interfaces for displaying the summary of the set of user reviews are illustrated in FIGS. 7-9. In the examples illustrated in FIGS. 7-9, the summary section (e.g., item 720 of FIG. 7, item 820 of FIG. 8, and item 920 of FIG. 9) shows the average review rating of 4 stars and the total number of user reviews of 168. [0027] User review engine 123 may identify a particular user review and/or cause a display of at least a portion of the particular user review. In some implementations, the particular user review may be identified by receiving an indication that a selection (e.g., hovering over, clicking, double-clicking, etc.) has been made on the cell that corresponds to the particular user review in a bar graph (e.g., as discussed herein with respect to bar graph engine 125). In response to the selection of the particular user review, user review engine 123 may cause the display of the at least a portion of the particular user review. The at least a portion of the particular user review may include, for example, a preview of the user review and/or a detailed view of the user review. The preview of the user review may represent a short version of the user review (e.g., the review title, the user ID, the review rating, the quality of the user review, etc.). The detailed view of the user review may represent a full version of the user review including, for example, the entire review content. In some implementations, different views of the user review may be displayed based on a type of selection made on the cell. In the example illustrated in FIG. 9, in response to the indication that a first type of selection (e.g., hovering over) is made on the cell (e.g., item 950 of FIG.9), a preview of the user review may be displayed such as item 923 of FIG. 9. On the other hand, in response to the indication that a second type of selection (e.g., clicking) is made on the cell (e.g., item 950 of FIG. 9), a detailed view of the user review may be displayed in the review section (e.g., item 921 of FIG. 9, item 721 of FIG. 7, or item 821 of FIG. 8).

[0028] User review engine 123 may identify a particular keyword, identify a subset of user reviews of the set of user reviews where the subset of user reviews relate to the particular keyword, and/or cause a display of at least a portion of the subset of user reviews. In some implementations, the particular keyword may be identified by receiving an indication that a selection (e.g., hovering over, clicking, double-clicking, etc.) has been made on the particular keyword among a set of keywords that frequently appear in the set of user reviews (e.g., as discussed herein with respect to keyword engine 124). In response to the selection of the particular keyword, user review engine 123 may identify the subset of user reviews that are related to the particular keyword. For example, the subset of user reviews may include at least one instance of the particular keyword appearing in the user review (e.g., in the review title, in the review content, etc.). At least a portion of the identified subset of the user reviews may be displayed. For example, an excerpt (e.g., the user ID, review rating, at least a portion of the review content, etc.) from each user review of the subset of the user reviews may be displayed. In some instances, the review content of each user review may have the particular keyword highlighted or made visually different from the rest of the review content. In the example illustrated in FIG. 8, when "Keyword 1" is selected by a user (e.g., item 831 of FIG. 8), the user review section (e.g., item 821 of FIG. 8, item 721 of FIG. 7, or item 921 of FIG. 9) may show a subset of user reviews that are related to "Keyword 1" where "Keyword 1" is highlighted or made visually different from the rest of the review content of each user review of the subset.

[0029] Keyword engine 124 may identify a set of keywords that appear in the set of user reviews and/or cause a display of the set of keywords. In the examples illustrated in FIGS. 7-9, the keywords section (e.g., item 730 of FIG. 7, item 830 of FIG. 8, or item 930 of FIG. 9) may show the set of keywords. In some implementations, a particular keyword of the set of keywords may be identified based on a frequency of appearance of the particular keyword in the set of user reviews. The frequency of appearance of the particular keyword in the set of user reviews may be determined in various ways. For example, if the particular keyword appears more than a threshold value in the set of user reviews, the particular keyword may be included in the set of keywords to be displayed. In another example, if the total number of user reviews that have the particular keyword reaches a threshold value, the particular keyword may be included in the set of keywords to be displayed.

[0030] In some instances, the color, shape, size, font, and/or other visual characteristics of a keyword to be displayed may vary depending on the frequency of appearance of that keyword in the set of user reviews. In one example, a first keyword that has a higher frequency of appearance than a second keyword may be shown larger than the size of the second keyword on the display. [0031] Keyword engine 124 may update and/or modify the display of the set of keywords based on the time period specified by the time slider bar (e.g., as discussed herein with respect to time bar engine 122). In some implementations, keyword engine 124 may identify, in the set of user reviews, user reviews associated with a timestamp that is outside (or inside) of the specified time period. Keyword engine 124 may update and/or modify the display of the set of keywords by having the identified user reviews to be excluded from (or included in) the set of user reviews. With this functionality, a user may learn that a product was initially (e.g., at the time the product was released) praised based on the set of keywords displayed. But as the user moves a bar control towards to the other end of the time slider bar, the set of keywords may be updated as some user reviews are included in and/or excluded from the set of user reviews based on the specified time period. The user may notice that the keyword "overheating" is shown in the set of keywords starting at 2 months after the product release date. This provides the user additional context and understanding of the set of user reviews.

[0032] Bar graph engine 125 may generate a bar graph comprising a bar composed of a set of cells that represent a subset of user reviews of the set of user reviews (e.g., obtained by data obtain engine 121). Each of the set of cells may correspond to an individual user review of the subset of user reviews. Example user interfaces for displaying the bar graph are illustrated in FIGS. 7-9. In the examples illustrated in FIGS. 7-9, the bar graph (e.g., item 740 of FIG. 7, item 840 of FIG. 8, or item 940 of FIG. 9) comprises 5 different bars (e.g., items 741-745 of FIG. 7, items 841-845 of FIG. 8, or items 941-945 of FIG. 9). Each of the bars may include a different subset of user reviews. In the example illustrated in FIG. 7, a first subset of user reviews for a first bar (e.g., item 741 of FIG. 7) may be associated with a first review rating (e.g., 5 stars), a second subset of user reviews for a second bar (e.g., item 742 of FIG. 7) may be associated with a second review rating (e.g., 4 stars), a third subset of user reviews for a third bar (e.g., item 743 of FIG. 7) may be associated with a third review rating (e.g., 3 stars), and so on. [0033] In some implementations, the set of cells may be arranged in the order of the quality of individual user reviews of the subset of user reviews. For example, higher quality reviews may be placed towards the top of the bar while lower quality reviews may be placed towards the bottom of the bar. Returning to the examples illustrated in FIGS. 7-9, the reviews (and their corresponding cells) with 75-100% review quality (e.g., 75-100% of users who viewed the user review found it helpful) are placed on the top of the bar, the reviews (and their corresponding cells) with 50- 75% review quality are placed below the reviews with 75-100% review quality, the reviews (and their corresponding cells) with 25-50% review quality are placed below the reviews with 50-75% review quality, and the reviews (and their corresponding cells with 0-25% review quality are placed at the bottom of the bar.

[0034] Bar graph engine 125 may cause a display of the bar graph comprising the bar composed of the set of cells. The length of the bar may represent the total number of user reviews in the subset of user reviews. In some implementations, the user reviews with differing quality may be shown visually differently (e.g., different color, shape, size, pattern, etc.) from each other. Returning to the example illustrated in FIG. 7, the bar (e.g., item 741 of FIG. 7) has four different patterns depending on the quality associated with each of the cells.

[0035] Bar graph engine 125 may update and/or modify the display of the bar graph based on the time period specified by the time slider bar (e.g., as discussed herein with respect to time bar engine 122). In some implementations, bar graph engine 125 may identify, in the set of user reviews, user reviews associated with a timestamp that is outside (or inside) of the specified time period. Bar graph engine 125 may update and/or modify the display of the bar graph by having the identified user reviews to be excluded from (or included in) the set of user reviews. With this functionality, a user may learn how the review ratings, quality, and/or other characteristics of the user reviews change over time as the user moves a bar control along the time slider bar. This provides the user additional context and understanding of the set of user reviews. [0036] Bar graph engine 125 may receive an indication that a selection (e.g., hovering over, clicking, double-clicking, etc.) has been made on a particular keyword of the set of keywords that are displayed (e.g., as discussed herein with respect to keyword engine 124). In response to the selection, bar graph engine 125 may cause at least one cell that corresponds to a user review having the particular keyword to appear visually different (e.g., different color, shape, pattern, borderline, flashing, etc.) from the rest of cells in the set of cells. In the example illustrated in FIG. 8, when the keyword "Keyword 1" is selected, the cells (e.g., items 851-856 of FIG. 8) corresponding to user reviews having the "Keyword 1" may be shown visually differently from the rest of the cells in the bar graph.

[0037] In performing their respective functions, engines 121 -125 may access data storage 129 and/or other suitable database(s). Data storage 129 may represent any memory accessible to user review visualization system 110 that can be used to store and retrieve data. Data storage 129 and/or other database may comprise random access memory (RAM), read-only memory (ROM), electrically-erasable

programmable read-only memory (EEPROM), cache memory, floppy disks, hard disks, optical disks, tapes, solid state drives, flash drives, portable compact disks, and/or other storage media for storing computer-executable instructions and/or data. User review visualization system 110 may access data storage 129 locally or remotely via network 50 or other networks.

[0038] Data storage 129 may include a database to organize and store data. Database 129 may be, include, or interface to, for example, relational database. Other databases, including file-based (e.g., comma or tab separated files), or query formats, platforms, or resources such as OLAP (On Line Analytical Processing), SQL (Structured Query Language), a SAN (storage area network), or others may also be used, incorporated, or accessed. The database may reside in a single or multiple physical device(s) and in a single or multiple physical location(s). The database may store a plurality of types of data and/or files and associated data or file description, administrative information, or any other data. [0039] FIG. 2 is a block diagram depicting an example user review visualization system 210. User review visualization system 210 may comprise a data obtain engine 221 , a keyword engine 222, a bar graph engine 223, and/or other engines. Engines 221-223 represent engines 121 , 124, and 125, respectively.

[0040] FIG. 3 is a block diagram depicting an example machine-readable storage medium 310 comprising instructions executable by a processor for visualization of user review data.

[0041] In the foregoing discussion, engines 121-125 were described as combinations of hardware and programming. Engines 121-125 may be implemented in a number of fashions. Referring to FIG. 3, the programming may be processor executable instructions 321-325 stored on a machine-readable storage medium 310 and the hardware may include a processor 311 for executing those instructions. Thus, machine-readable storage medium 310 can be said to store program instructions or code that when executed by processor 311 implements user review visualization system 110 of FIG. 1.

[0042] In FIG. 3, the executable program instructions in machine-readable storage medium 310 are depicted as data obtain instructions 321 , time bar instructions 322, user review instructions 323, keyword instructions 324, and bar graph instructions 325. Instructions 321-325 represent program instructions that, when executed, cause processor 311 to implement engine 121-125, respectively.

[0043] FIG. 4 is a block diagram depicting an example machine-readable storage medium 410 comprising instructions executable by a processor for visualization of user review data.

[0044] In the foregoing discussion, engines 121-125 were described as combinations of hardware and programming. Engines 121-125 may be implemented in a number of fashions. Referring to FIG. 4, the programming may be processor executable instructions 421-423 stored on a machine-readable storage medium 410 and the hardware may include a processor 411 for executing those instructions. Thus, machine-readable storage medium 410 can be said to store program instructions or code that when executed by processor 411 implements user review visualization system 110 of FIG. 1.

[0045] In FIG. 4, the executable program instructions in machine-readable storage medium 410 are depicted as data obtain instructions 421 , bar graph instructions 422, and user review instructions 423. Instructions 421-423 represent program instructions that, when executed, cause processor 411 to implement engines 121 , 125, and 123, respectively.

[0046] Machine-readable storage medium 310 (or machine-readable storage medium 410) may be any electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. In some implementations, machine-readable storage medium 310 (or machine-readable storage medium 410) may be a non-transitory storage medium, where the term "non-transitory" does not encompass transitory propagating signals. Machine-readable storage medium 310 (or machine-readable storage medium 410) may be implemented in a single device or distributed across devices. Likewise, processor 311 (or processor 411) may represent any number of processors capable of executing instructions stored by machine-readable storage medium 310 (or machine-readable storage medium 410). Processor 311 (or processor 411) may be integrated in a single device or distributed across devices. Further, machine-readable storage medium 310 (or machine- readable storage medium 410) may be fully or partially integrated in the same device as processor 311 (or processor 411 ), or it may be separate but accessible to that device and processor 311 (or processor 411 ).

[0047] In one example, the program instructions may be part of an installation package that when installed can be executed by processor 311 (or processor 411) to implement user review visualization system 110. In this case, machine-readable storage medium 310 (or machine-readable storage medium 410) may be a portable medium such as a floppy disk, CD, DVD, or flash drive or a memory maintained by a server from which the installation package can be downloaded and installed. In another example, the program instructions may be part of an application or applications already installed. Here, machine-readable storage medium 310 (or machine-readable storage medium 410) may include a hard disk, optical disk, tapes, solid state drives, RAM, ROM, EEPROM, or the like.

[0048] Processor 311 may be at least one central processing unit (CPU), microprocessor, and/or other hardware device suitable for retrieval and execution of instructions stored in machine-readable storage medium 310. Processor 311 may fetch, decode, and execute program instructions 321-325, and/or other instructions. As an alternative or in addition to retrieving and executing instructions, processor 311 may include at least one electronic circuit comprising a number of electronic components for performing the functionality of at least one of instructions 321-325, and/or other instructions.

[0049] Processor 411 may be at least one central processing unit (CPU), microprocessor, and/or other hardware device suitable for retrieval and execution of instructions stored in machine-readable storage medium 410. Processor 411 may fetch, decode, and execute program instructions 421-423, and/or other instructions. As an alternative or in addition to retrieving and executing instructions, processor 411 may include at least one electronic circuit comprising a number of electronic components for performing the functionality of at least one of instructions 421-423, and/or other instructions.

[0050] FIG. 5 is a flow diagram depicting an example method 500 for visualization of user review data. The various processing blocks and/or data flows depicted in FIG. 5 (and in the other drawing figures such as FIG. 6) are described in greater detail herein. The described processing blocks may be accomplished using some or all of the system components described in detail above and, in some implementations, various processing blocks may be performed in different sequences and various processing blocks may be omitted. Additional processing blocks may be performed along with some or all of the processing blocks shown in the depicted flow diagrams. Some processing blocks may be performed simultaneously. Accordingly, method 500 as illustrated (and described in greater detail below) is meant be an example and, as such, should not be viewed as limiting. Method 500 may be implemented in the form of executable instructions stored on a machine-readable storage medium, such as storage medium 310, and/or in the form of electronic circuitry.

[0051] In block 521 , method 500 may include obtaining a set of user reviews associated with an item from at least one source. A user review of the set of user reviews may comprise at least one of: a review rating (e.g., indicating a degree of positiveness or negativeness of the review content), review content, identification information of the user who created the user review (e.g., user name, user ID, Internet Protocol (IP) address, etc.), and information related to quality of the user review (e.g., helpfulness or unhelpfulness of the user review). The quality of a user review may be determined based on a degree of reputations associated with the user who created the user review (e.g., the user who frequently provided user reviews for various items in the past may have a higher reputation score than another user who provided the user review for the first time), feedback on the user review given by at least one user other than the user who created the user review (e.g., other users may view the user review and answer to a question that asks whether the user review was helpful or unhelpful), a timestamp associated with the user review (e.g., a more recent user review may indicate a better quality review), a length (or size) of the user review (e.g., a longer user review may indicate a better quality review), and/or other factors related to the reliability, credibility, and/or validity of the user review.

[0052] In block 522, method 500 may include generating a bar graph comprising a first bar composed of a first set of cells that represent a first subset of user reviews of the set of user reviews (e.g., obtained in block 521). Each of the first set of cells may correspond to an individual user review of the first subset of user reviews. The set of cells may be arranged in the order of the quality of individual user reviews of the first subset of user reviews. For example, higher quality reviews may be placed towards the top of the bar while lower quality reviews may be placed towards the bottom of the bar. [0053] Referring back to FIG. 1 , data obtain engine 121 may be responsible for implementing block 521. Bar graph engine 125 may be responsible for implementing block 522.

[0054] FIG. 6 is a flow diagram depicting an example method 600 for visualization of user review data. Method 600 as illustrated (and described in greater detail below) is meant be an example and, as such, should not be viewed as limiting. Method 600 may be implemented in the form of executable instructions stored on a machine- readable storage medium, such as storage medium 210, and/or in the form of electronic circuitry.

[0055] In block 621 , method 600 may include obtaining a set of user reviews associated with an item from at least one source. A user review of the set of user reviews may comprise at least one of: a review rating, review content, identification information of the user who created the user review, and information related to quality of the user review.

[0056] In block 622, method 600 may include generating a bar graph comprising a first bar composed of a first set of cells that represent a first subset of user reviews of the set of user reviews (e.g., obtained in block 521 ) and a second bar composed of a second set of cells that represent a second subset of user reviews of the set of user reviews. The first subset of user reviews and the second subset of user reviews may be associated with a different review rating. In the example illustrated in FIG. 7, the bar graph comprises 5 different bars (e.g., items 741-745 of FIG. 7). Each of the bars may include a different subset of user reviews. In this example, the first subset of user reviews for the first bar (e.g., item 741 of FIG. 7) may be associated with a first review rating (e.g., 5 stars), the second subset of user reviews for the second bar (e.g., item 742 of FIG. 7) may be associated with a second review rating (e.g., 4 stars), and so on.

[0057] In block 623, method 600 may include causing the bar graph (e.g., generated in block 622) to be displayed via a user interface.

[0058] In block 624, method 600 may include receiving, via the user interface, an indication that a selection (e.g., hovering over, clicking, double-clicking, etc.) has been made on a cell that represents a particular user review. In block 625, in response to the indication that the selection has been made on the cell, method 600 may include causing at least a portion of the user review to be displayed via the user interface. The at least a portion of the user review may include, for example, a preview of the user review and/or a detailed view of the user review. The preview of the user review may represent a short version of the user review (e.g., the review title, the user ID, the review rating, the quality of the user review, etc.). The detailed view of the user review may represent a full version of the user review including, for example, the entire review content.

[0059] In some implementations, different views of the user review may be displayed based on a type of selection made on the cell. In the example illustrated in FIG. 9, in response to the indication that a first type of selection (e.g., hovering over) is made on the cell (e.g., item 950 of FIG.9), a preview of the user review may be displayed such as item 923 of FIG. 9. On the other hand, in response to the indication that a second type of selection (e.g., clicking) is made on the cell (e.g., item 950 of FIG. 9), a detailed view of the user review may be displayed in the review section (e.g., item 921 of FIG. 9).

[0060] Referring back to FIG. 1 , data obtain engine 121 may be responsible for implementing block 621. Bar graph engine 125 may be responsible for implementing blocks 622 and 623. User review engine 123 may be responsible for implementing blocks 624 and 625.

[0061] FIG. 7 is a diagram depicting an example user interface 700 for visualization of user review data. FIG. 8 is a diagram depicting an example user interface 800 for visualization of user review data. FIG. 9 is a diagram depicting an example user interface 900 for visualization of user review data. User interface 700

(and other user interfaces described herein) may be used to cause various actions to be performed by user review visualization system 1 0.

[0062] FIGS. 7-9 are discussed herein with respect to FIG. 1.

[0063] The foregoing disclosure describes a number of example implementations for visualization of user review data. The disclosed examples may include systems, devices, computer-readable storage media, and methods for visualization of user review data. For purposes of explanation, certain examples are described with reference to the components illustrated in FIGS. 1-4. The functionality of the illustrated components may overlap, however, and may be present in a fewer or greater number of elements and components.

[0064] Further, all or part of the functionality of illustrated elements may co-exist or be distributed among several geographically dispersed locations. Moreover, the disclosed examples may be implemented in various environments and are not limited to the illustrated examples. Further, the sequence of operations described in connection with FIGS. 5-6 are examples and are not intended to be limiting. Additional or fewer operations or combinations of operations may be used or may vary without departing from the scope of the disclosed examples. Furthermore, implementations consistent with the disclosed examples need not perform the sequence of operations in any particular order. Thus, the present disclosure merely sets forth possible examples of implementations, and many variations and modifications may be made to the described examples. All such modifications and variations are intended to be included within the scope of this disclosure and protected by the following claims.

Claims

1. A method for visualization of user review data, the method comprising: obtaining a set of user reviews associated with an item from at least one source, a user review of the set of user reviews comprises at least one of: a review rating, review content, identification information of a user who created the user review, and information related to quality of the user review; and

generating a bar graph comprising a first bar composed of a first set of cells that represent a first subset of user reviews of the set of user reviews by arranging the first set of cells in the order of the quality of individual user reviews of the first subset of user reviews.

2. The method of claim 1 , wherein the first subset of user reviews is associated with a first review rating and a second subset of user reviews of the set of user reviews is associated with a second review rating, further comprising:

generating the bar graph comprising a second bar composed of a second set of cells that represent the second subset of user reviews by arranging the second set of cells in the order of the quality of individual user reviews of the second subset of user reviews.

3. The method of claim 1 , wherein a first user review of the first subset of user reviews and a second user review of the first subset of user reviews are associated with a different quality, further comprising:

causing the bar graph to be displayed via a user interface, wherein a first cell representing the first user review appears visually different from a second cell representing the second user review.

4. The method of claim 1 , further comprising:

determining the quality of the user review based on at least one of: a degree of reputation associated with the user who created the user review, feedback on the user review given by at least one user other than the user who created the user review, a timestamp associated with the user review, and a length of the user review.

5. The method of claim 1 , further comprising:

causing the bar graph to be displayed via a user interface;

receiving, via the user interface, an indication that a first type of selection has been made on a cell that represents the user review; and

in response to the indication that the first type of selection has been made on the cell that represents the user review, causing a preview of the user review to be displayed via the user interface.

6. The method of claim 1 , further comprising:

receiving, via the user interface, an indication that a second type of selection has been made on a cell that represents the user review; and

in response to the indication that the second type of selection has been made on the cell that represents the user review, causing a detailed view of the user review to be displayed via the user interface.

7. A non-transitory machine-readable storage medium comprising instructions executable by a processor of a computing device for visualization of user review data, the machine-readable storage medium comprising:

instructions to obtain a set of user reviews associated with an item from at least one source;

instructions to cause a display of a bar graph comprising a bar composed of a set of cells, each of the set of cells corresponding to an individual user review of the set of user reviews;

instructions to receive an indication that a selection has been made on a cell that corresponds to a particular user review of the set of user reviews; and in response to the indication that the selection has been made on the cell that corresponds to the particular user review, instructions to cause the display of at least a portion of the particular user review.

8. The non-transitory machine-readable storage medium of claim 7, further comprising:

instructions to cause the display of a time slider bar comprising at least one bar control that is used to specify a time period;

instructions to identify, in the set of user reviews, user reviews associated with a timestamp that is outside of the specified time period; and

instructions to update the display of the bar graph by excluding the identified user reviews from the set of user reviews.

9. The non-transitory machine-readable storage medium of claim 7, further comprising:

instructions to cause the display of a set of keywords that appear in the set of user reviews;

instructions to update the display of the set of keywords by excluding, from the set of keywords, the keywords that appear in the identified user reviews.

10. The non-transitory machine-readable storage medium of claim 7, wherein the particular user review of the set of user reviews comprises at least one of: a review rating, review content, identification information of a user who created the particular user review, a timestamp, and information related to quality of the particular user review.

11. The non-transitory machine-readable storage medium of claim 10, wherein the set of cells are arranged in the order of the quality of individual user reviews corresponding to the set of cells.

12. A system for visualization of user review data comprising:

a data obtain engine to obtain a set of user reviews associated with an item from at least one source;

a keyword engine to cause a display of a set of keywords that frequently appear in the set of user reviews;

a bar graph engine to cause the display of a bar graph comprising a bar composed of a set of cells, each of the set of cells corresponding to an individual user review of the set of user reviews;

the keyword engine to receive an indication that a selection has been made on a keyword of the set of keywords; and

in response to the indication that the selection has been made on the keyword of the set of keywords, the bar graph engine to cause at least one cell that corresponds to a user review having the keyword to appear visually different from the rest of cells in the set of cells.

13. The system of claim 12, wherein a particular user review of the set of user reviews comprises at least one of: a review rating, review content, identification information of a user who created the particular user review, a timestamp, and information related to quality of the particular user review.

14. The system of claim 13, wherein the set of cells are arranged in the order of the quality of individual user reviews corresponding to the set of cells.

15. The system of claim 13, further comprising:

the bar graph engine to receive an indication that a selection has been made on a cell that represents the particular user review; and in response to the indication that the selection has been made on the cell that represents the particular user review, a user review engine to cause the display of at least a portion of the user review.