Method for tailored mobile application rating insights

A system and method for customizing mobile device application reviews that collects attributes of the mobile device of the reviewing user along with the actual review. When a potential user accesses the reviews, corresponding information is collected from the mobile device of the potential user and then used to filter the reviews for the target application. The attributes can include mobile device type, mobile device operating system, location, mobile device service provider, mobile device storage capacity remaining, mobile device signal strength, and type of mobile connection. The filtering of reviews can be managed manually by the potential user or automatically by using intelligent analysis of the attributes and the content of the reviews in order to provide enhanced insight to the potential user.

BACKGROUND

The present invention relates to mobile application selection and, more specifically, to a method that presents users of mobile devices with application ratings and reviews that most closely apply to their own set of circumstances.

With over 1.2 million iOS™ apps and 1.3 million Android® applications to choose from, finding the right application is a challenge for every mobile device user. Many users rely on application reviews to evaluate if an application is right for them. Application reviews are submitted by other mobile device users who have downloaded the application onto their own mobile device. The review typically consists of two parts: a star rating (typically 1 through 5 stars) and, optionally, a written description of the experience of the user with the application. The application reviews are made available to potential uses in an application store.

There are many criteria that define the suitability of an application for a given user. One user may rate an application poorly (a 1 star rating) if, for example, it performs slowly on their device. Another user, with a newer device, may not experience these problems and rate the application highly (5 stars). Similarly, a skiing application may receive contrasting ratings based on geography—a user in Colorado may love it (it does, after all, feature their favorite resort of Breckenridge) while a user in North Carolina may be disappointed (their local resort, Sugar Mountain, is nowhere to be found). Clearly there are many factors influencing application ratings, and application stores provide very little customization to cut out the noise and present a user with reviews and ratings of applications that apply to their specific situation. For example, the Google Play™ application store allows users to filter reviews based on application version (latest or all) and to order the reviews according to three criteria: newest, rating, and helpful. the Google Play™ application store also offers an additional filter for mobile devices—By Device—which shows only reviews that were received by those using the specific device of the user. These limited filtering and sorting capabilities leave users with potentially hundreds of reviews to scan through, many of which do not apply to their situation. Users in a different city, or running a different OS version, or on a different network carrier, or with differing storage capacities may all have different impressions of how an application meets their needs. Thus, there is a need in the art for a system and method of presenting potential users of mobile devices with applications ratings and reviews that most closely apply to their own specific set of circumstances.

SUMMARY

The present invention comprises a system that is enabled to capture a first set of specific characteristics about a mobile device when a reviewer uploads a mobile device application review through the mobile device, and then filter mobile device application reviews based on the captured first set of specific characteristics when a potential user of the application requests reviews for a given application. In various embodiments, the captured set of specific characteristics of the mobile device can comprise the manufacturer of the mobile device, the OS level in which the mobile device operates, the geographical location in which the mobile device application review was uploaded, the wireless service provider of the mobile device, the connection type at the time when the mobile device application review was uploaded, the connection strength at the time when the mobile device application review was uploaded, and the available storage of the mobile device at the time when the mobile device application review was uploaded. In one embodiment, the system receives a mobile device application selection from a mobile application catalogue/store from a user looking to download a mobile device application, and displays the filtered plurality of mobile device application reviews about the selected mobile device application, wherein the filtering is based on finding similarities between the captured first set of specific characteristics associated with the mobile device application review and a second set of specific characteristics about a second mobile device owned by the user. In another embodiment, the system identifies rating, sentiment, and semantic patterns within the content of amongst the filtered plurality of mobile device application reviews, and provides insight about the mobile device application that is tailored to the potential user based on the analysis of the identified rating, sentiment, and semantic patterns.

DETAILED DESCRIPTION

Referring now to the drawings, wherein like reference numerals refer to like parts throughout, there is seen inFIG. 1a system10for presenting potential users of mobile devices with applications ratings and reviews that most closely apply to their own specific set of circumstances. System10comprises a mobile application repository/store12and a cognitive system14, such as a server, interconnected to application store12. Application store12includes a component that is configured to capture ratings and reviews of apps, and that is extended to include new attributes as described herein with respect to the present invention. Application store12also includes a component for searching reviews based on the criteria of the present invention. Cognitive system14is preferably implemented as a remote server or as an extension of existing application repositories. Cognitive system14includes software used for processing and analysis of shared attributes to develop insights according to the present invention. Finally, system10comprises a mobile device16that includes a component for a reviewing user to view application ratings based on shared attributes, a component for a user to search application ratings combining multiple shared attributes, and a component for a user to view insights derived from comparative analysis of shared attributes. In practice, a mobile device16is initially used by a user of a particular application to submit an app review18about the application, and a mobile device20is used by a potential user of the application to assess a plurality of previously collected app reviews22for the application prior to purchasing or downloading the application.

In addition to app review18, metadata comprising reviewer attributes24of mobile device16is collected by system10when a user of an application submits an application rating and review to an application store. Conventionally, the user provides a star rating (typically 1-5 stars) of the application and a written review of the application. Some identifying information may also be retrieved from the device of the user and sent to the application store, such as the version of the application being rated, the type of mobile device sending the review, and the name of the user of the device. The present invention further requires that mobile device16provide the mobile device OS level, current geolocation, cellular provider, current connection type (WiFi, 4G LTE, etc.), current connection signal strength, and the amount of available storage on the device. In one embodiment, this additional information is provided by the mobile device in response to a request from the application store. In another embodiment, this information is collected and provided by application residing on the mobile device.

Next, when a potential user of the application navigates to the application in the application store and requests reviews for a particular application, the same information is collected from the mobile device of the potential user. More specifically, requester attributes26can comprise the mobile device OS level, current geolocation, cellular provider, current connection type (WiFi, 4G LTE, etc.), current connection signal strength, and the amount of available storage on the device is collected and provided to application store from the mobile device of the potential user as described above.

Finally, the newly collected information is then used to present the potential user with a customized set of reviews according to the mobile device specific information of the potential user. This selection and arrangement of reviews can be provided in various ways. For example, in a first embodiment, the potential user may be provided with a menu of manual options for sorting the reviews using categories corresponding to the user specific information collected from users that provided reviews and by the potential user. More particularly, the potential user may be given the option to sort reviews according to whether the mobile devices of the reviewing users have matching specific information to that of the potential user sorting the reviews. For example, the reviews for a particular application can be filtered according to criteria such as “My Device” (reviewers who used the same mobile device as the potential user), “My OS” (reviewers who ran the same mobile operating system as the potential user), “My Location” (reviewers who were located in the same region as the potential user), “My Provider” (reviewers who used the same cellular provider as the potential user), “My Storage” (reviewers who had about the same amount of free storage space on their device as the potential user), “My Signal” (reviewers who had about the same amount of signal strength—Wifi or cellular—as the potential user), or combinations thereof. It should be recognized by those of skill in the art the additional categories of information may be used, and that the categories of information could be labelled with any term that easily identifies the nature of the sorting criteria to the potential user. As seen inFIG. 2, a user can select application reviews to match a particular attribute30, illustrated as “My Device” to filter the reviews32to only those with matching attributes, e.g., reviews submitted using a mobile device that is the same as “My Device.” As seen inFIG. 3, a custom filter option34may be provided to allow a user to filter using any desired combination of attributes, such as “Connection”36and Signal Strength”38together. Additional attributes can be considered through self-learning of a user over time, by learning the preferences of the user, and refining the application reviews presented to them.

In another embodiment, the user reviews can be automatically sorted using intelligent algorithms so that the potential user is presented with tailored insights as to the suitability of a given application to the particular set of circumstances of the potential user. These insights may be generated through comparative analysis utilizing rating analysis, sentiment analysis, and natural language processing. As an example, the criteria could be weighted depending on the nature of the application that is the subject of the reviews. Table 1 below identifies how the information collected according to the present invention can be intelligently weighted for automatic sorting and presentation to the potential user.

TABLE 1CriteriaHigher Significance WeightLower Significant WeightMy DeviceGraphics and CPU intensive apps suchNon graphic and CPU intensiveas a gaming application that is knownapps, such as apps that areto lag prior on older model chipsets orknown to perform well on anythat requires features added to newerdevice or apps that are not tiedmodel phones.to features found only on newermobile devices.My OSApps that use functionality that is onlyApps where functionality is notoffered by or limited to certain OStied to particular OS levels.levels.My LocationLocation specific apps, such asApps where location is not arestaurant couponing apps that arefactor, such as games ordirected to regional chains or localnavigation apps with worldwideestablishments not found in all cities.capabilities.My StorageApps that require a large amount ofApps that require insignificantstorage (for example <1 GB) may notamount of storage equallybe appealing to a user with limitedappealing to mobile devicestorage.users with highly or lowlyutilized storage space.

The appropriate weighting application to the criteria for a given application and a given user can be determined by first retrieving a statistically significant portion of or even all ratings and reviews for a given application where the reviewer matches at least one shared attributes of the user. Therefore all ratings and reviews where reviewers used the same device, same OS, same location, and so forth are retrieved. Next, a comparative analysis is performed to identify any common patterns with the ratings and reviews. This comparative analysis looks for commonalities that occur with a single shared attribute, and also that only occur when multiple shared attributes are combined. Confidence levels can also be assigned based upon the likelihood that an insight is relevant to a mobile device user. The comparative analysis may use techniques such as a rating analysis that analyzes the star ratings assigned to the application, a sentiment analysis that analyzes the sentiment expressed in written application reviews to determine positive or negative sentiment for given terms, and natural language processing that analyzes written comments in application reviews to best determine the reason for a rating.

The resulting insights based on any identified patterns may then be presented to the potential user. For example, the potential user may be presented with a summary analysis report such as that seen inFIG. 4. By clicking the “More Info” seen inFIG. 4, the application reviews that support these insights may be presented to the potential user for review. These application reviews thus represent a subset of the one or more shared attributes that would have been shown to the user in the first, manual embodiment. In this embodiment, the potential user is given insight into which shared attributes have been determined as being important for a given application and which are not.

One example of an insight that can be derived from the stored data is “70% of reviewers in your location and with your cellular provider were unhappy with the speed of this application.” This insight may be determined by using a rating analysis to glean that application star ratings were 1.5 stars below a baseline or average when the reviews matched both My Location and My Provider. If no other single or combined shared attribute produces as big of a rating decline, it is possible to determine that reviews matching the My Location and My Provider attributes are important. Sentiment Analysis may then be used to determine the cause of the lower rating. For example, if a textual search of the reviews identified that the terms “performance” and “speed” had the highest negative sentiment associated with them and occurred in 70% of application reviews that matched My Location and My Provider, then the potential user can be provided with a message indicating that “70% of reviewers in your location and with your cellular provider were unhappy with the speed of this application.”

Similarly, the insight “85% of reviewers with a similar amount of free storage rated the application 3 stars or less due to the large size of the app” may be derived by performing natural language processing to identify, through lemmatization, that many reviews referred to the size of the application, with comments such as bloated”, “large install size”, “too big”, “not enough space,” and similar derivatives, with these references clustered in reviews matching My Storage (75% or more of storage being used). Rating analysis may then be used to determine that star ratings for reviews were 3 stars or less in 85% of reviews matching My Storage.

Referring toFIG. 5, a process40for customizing mobile device application reviews according to the present invention begins with a pre-configuration collection42where users submit application store ratings and reviews, additional metadata is collected from the mobile device of the reviewing user and sent to the application store, such as device, OS, location, provider, storage, signal, connection as described above. Next, a potential user on mobile device launches an application store and views the reviews for a specific application44.

In the manual embodiment of the present invention, all application reviews and ratings are made available to the user, who is then allowed to filter the application reviews46based on shared attributes using a graphical interface that provides filter criteria keyed to the shared attributes. For example, if the user selects the location criteria, the mobile device retrieves current geolocation using GPS and sends to application store, the application store receives the geolocation and compares to the geolocation stored for all reviews of the application, and only application reviews written within a defined area of that location (such as within 10 miles, within the same city, or within the same state or country) are returned to the mobile device for display48to the potential user. The potential user may then filter the application reviews based on additional attributes by repeating step46.

In the intelligent embodiment of the present invention, insights are generated50when a user selects a given application by having the mobile device of the potential user send real time information about the mobile device along with the request for reviews. The application store then retrieves all reviews and ratings for the desired application that match the metadata from the mobile device and pass this information to the cognitive system. The cognitive system uses rating analysis52, sentiment analysis54, and natural language processing56to look for patterns in the subset of reviews and ratings. The cognitive system then combines the results to develop one or more insights for display to the potential user and confidence levels may be assigned based upon the likelihood that the insight is relevant to the potential user. The insights and collaborating application reviews from which the insights were generated are then sent from the cognitive system to the mobile device for display of the insights58along with the pertinent application reviews so that the potential user can view the specific reviews that contributed to the insights.

In a further embodiment of the invention, system10can provide recommendations on ideal operating conditions for a given mobile application, regardless of the mobile device user. For example, an analytics engine can retrieve all ratings and reviews for a given application, along with the associated attributes (mobile device, OS level, cellular provide, connection method, signal strength, available storage) for each review. The analytics engine then performs a comparative analysis to identify common patterns across all ratings regardless of any attribute matching using rating analysis, sentiment analysis, and natural language processing, as described above. The analytics engine develops insights based on identified patterns related to the combination of attributes where the mobile application runs optimally and the combination of attributes where the mobile application runs sub-optimally. As an example, the engine might determine the insight that Mobile App A runs best on mobile operating system iOS V7.1 and above, with the most common issues reported below this OS level were a lack of support for Feature X and frequent crashes. This insight could be determined by using Rating Analysis to determined that reviewers running less than iOS V7.1 gave the application a 50% lower than average star rating than users at iOS V7.1 or above, Sentiment Analysis to determined that the most commonly expressed negative sentiment for users below iOS V7.1 were related to performance, and natural language processing to determine that the most commonly expressed comments for those using below iOS V7.1 related to lack of Feature X. This embodiment of the present invention could be used by mobile application users considering purchasing an application to determine that they should upgrade to iOS V7.1 before using the application for the optimum experience. This embodiment of the present invention could also be used by mobile application developers to learn which attributes contribute most to positive and negative experiences with the application for the purposes of making adjustments to future versions of application accordingly.

Referring toFIG. 6, system10may implemented using a reviewing user module60that is programmed to collect app reviews18and reviewer attributes24for storage in an app review database62. A cognitive module64is responsible for managing the app review database62and responding to potential user review requests that are received by review delivery module66along with requester attributes26. Using requester attributes26, cognitive module64can perform the customized review processes described above and identify relevant reviews for review delivery module66to provide to a user.