Delivering advertisements based on user sentiment and learned behavior

Provided are embodiments including a method, system, and computer program product for identifying how to deliver advertisements to a user based on the user sentiment and learned behavior. The embodiments provide for collecting information on the sentiment of a user, monitoring user interactions based at least in part on the collected emotional information on the sentiment of the user, and determining a pattern of user interactions with one or more applications of a user device based on the sentiment information. The embodiments also provide for determining a pattern of user responses to an advertisement based on the sentiment information, and generating a profile based at least in part on the pattern of user interactions and the pattern of user responses.

BACKGROUND

The present invention generally relates to computing networks, and more specifically, to embodiments for delivering advertisements based on user sentiment and learned behavior.

Advertisement can be useful in attracting a user's attention to a particular service and/or product. In today's environment, advertisements are presented to users in a variety of ways. For example, advertisements can be provided to the user in a banner of an email, pop-up windows, social media applications, games, video clips, and the like. The selection of advertisements can cover a wide range and can be targeted to a particular user based on a variety of factors. For example, advertisements can be targeted to a particular user based on their age, association with one or more organizations, or a search history.

SUMMARY

Embodiments of the present invention are directed to a computer-implemented method for delivering advertisements based on user sentiment and learned behavior. A non-limiting example of the computer-implemented method includes collecting emotional information of a user; monitoring user interactions based at least in part on the collected emotional information of the user; determining a pattern of user interactions with one or more applications of a user device based on the emotional information; determining a pattern of user responses to an advertisement based on the emotional information; and generating a profile based at least in part on the pattern of user interactions and the pattern of user responses.

Embodiments of the present invention are directed to a system for delivering advertisements based on user sentiment and learned behavior. A non-limiting example of the system includes a storage medium, the storage medium being coupled to a processor, where the processor is configured to collect emotional information of a user; monitor user interactions based at least in part on the collected emotional information of the user; determine a pattern of user interactions with one or more applications of a user device based on the emotional information; determine a pattern of user responses to advertisements based on the emotional information; and generate a profile based at least in part on the pattern of user interactions and the pattern of user responses.

Embodiments of the invention are directed to a computer program product for delivering advertisements based on user sentiment and learned behavior, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to perform a method. A non-limiting example of the method includes collecting emotional information of a user; monitoring user interactions based at least in part on the collected emotional information of the user; determining a pattern of user interactions with one or more applications of a user device based on the emotional information; determining a pattern of user responses to an advertisement based on the emotional information; and generating a profile based at least in part on the pattern of user interactions and the pattern of user responses.

DETAILED DESCRIPTION

Characteristics are as follows:

Deployment Models are as follows:

Turning now to an overview of technologies that are more specifically relevant to aspects of the invention, currently advertisement services select content to provide to a user based on the text provided in a search query or content that is frequently visited and/or liked (interest) content. For example, a user may send an email related to a sport and an advertisement related to the sport can be provided to a user. The advertisement can provide an option to buy tickets to an upcoming event or vendors that sell sports clothing related to the preferred team. In addition, a user that is visiting a particular location advertisements that are provided to the user can include advertisements for restaurants, activities, etc. that are within proximity of the user. Other techniques are used to direct advertisements to the user.

In today's environment, advertisement services are limited in the information that can be obtained from a user. For example, the current techniques fail to factor contextual information related to the user such as the user's current mood and/or emotional state. The current techniques also fail to consider a pattern of a user's interactions with applications based on the current mood. Finally, the techniques fail to provide a link between the detected user's mood/emotional state and the user's patterned behavior of interacting with a mobile device, computing device, and/or other user device. Because these factors are not used, the effectiveness of the advertisement received by the user is tracked and is not leveraged to select advertisements for the user.

Turning now to an overview of the aspects of the invention, one or more embodiments of the invention address the above-described shortcomings of the prior art by providing detecting a user's current emotional state and mapping the user's emotional state to a user's behavior when interacting with a user device. The effectiveness of an advertisement is variable and on many occasions, if the target audience is not in the appropriate emotional state they may not follow the advertisement. In addition, users may exhibit behaviors during a specific emotional state where they to become very susceptible to certain advertisements.

The above-described aspects of the invention address the shortcomings of the prior art by identifying user behaviors during a specific emotional state and determining patterns of how the user responds to various types of advertisements to push advertisements to the user in a targeted manner. The techniques described herein generate a profile based on the determined patterned behavior and the corresponding emotional state of a user and to target advertisements for the user. The effectiveness of an advertisement can be indicated by a user making a purchase based on the advertisement or a user reading/watching the entire advertisement that is provided to the user.

Thus, as configured inFIG. 3, the system100includes processing capability in the form of processors101, storage capability including the system memory114and mass storage104, input means such as keyboard109and mouse110, and output capability including speaker111and display115. In one embodiment, a portion of system memory114and mass storage104collectively store an operating system to coordinate the functions of the various components shown inFIG. 3.

Now referring toFIG. 4, a system400for delivering advertisements based on user sentiment and learned behavior in accordance with one or more embodiments is shown. It is to be understood the system400can include one or more elements shown inFIGS. 1-3.

The system400includes a user device402, and the user device402can be equipped with one or more sensors404to detect an emotion of a user. In one or more embodiments of the invention, the emotion can be determined based on detecting a person's speech, facial expressions, physiological signals, etc. The system400and/or user device402can include one or more sensors404for sensing an emotion of the user. In other embodiments of the invention, the sensor404can be located external to a user device402such as a wearable device, audio receiving information, the video camera that can be used to obtain data to perform an image analysis to determine a user emotion. The sensors404can be equipped in rooms to determine user emotions, jewelry, wearables, etc. In addition, the user emotion can be detected by a user's social network data and/or feeds, text messages, etc.

The tone which can indicate an emotion of a user can be detected by an audio device such as a microphone. In addition, emotional information can be determined by monitoring the text entered on a user device by filtering the text for keywords such as performing the sentimental analysis. The sentiment analysis module may be provided by an application program interface (API). The above mentioned APIs are mentioned for exemplary purposes. Any cognitive AI can be utilized within the sentiment analysis module. The sentiment analysis module can process natural language to incorporate both a linguistic and statistical analysis in evaluating the context of a communication. In text analysis, the sentiment is the attitude or opinion expressed toward something. Sentiment can be positive, “sounds good”, negative, “this is bad”, or neutral. Sentiment can be calculated based on keywords extracted and evaluated at a keyword level. Additionally, the sentiment analysis may be capable of identifying negations, such as the term “not” and the change in sentiment from the keyword “good” when the phrase is “not” “good”. The sentiment analysis may consider intensity when the terms “very” or other adjectives are utilized in combination with a keyword. Additionally, the keywords may be weighted. For instance, a positive phrase such as “like” will have a predefined positive weight, whereas the phrase “love” might have a higher predefined positive weight. Additionally, negative weights may be afforded negative phrases such as “dislike” would have a predefined negative weight and the phrase “hate” might have a higher negative weight. The sentiment analysis module can evaluate the content to provide a sentiment level. This sentiment level may also include an intensity value.

The tonal analysis module may include a tonal analyzer service, for example. The tonal analysis module can use linguistic analysis to detect three types of tones from the text. The natural language content is analyzed by the tonal analysis module for determining the emotional impact, social tone, and writing style that the content projects. The tonal analysis module may provide tonal scores for emotional tone, social tone, and language tone. For emotional tone, the tonal analysis module may utilize the emotions for “joy”, “fear”, “sadness”, “disgust” and “anger”. Each natural language element is evaluated with respect to each emotion. Each emotion may be evaluated from lower values having a value range that indicates if that emotion is less likely to appear as perceived or alternatively to a higher value range if the emotion is more likely to be perceived with respect to each natural language content. Other emotions may be utilized as well as a different value score.

For social tone, the five elements of openness, conscientiousness, extraversion, agreeableness, and emotional range are utilized. Openness is evaluated as the extent a person is open to experience a variety of activities. This trait may be provided a value range indicating that it is more likely to be perceived as no-nonsense, straightforward, blunt and obvious, alternatively, a higher value range may be provided if the content indicates that it will be perceived as intellectual, curious, emotionally-aware, or imaginative. Conscientiousness is evaluated as the tendency to act in an organized or thoughtful way. This trait may be provided a value range if the presentation is perceived as spontaneous, laid-back, reckless, unmethodical or disorganized, or alternatively, a higher value range may be provided if the content is perceived as disciplined, dutiful, or confident. Extraversion is evaluated as the tendency to seek stimulation in the company of others. This trait may be provided a value range if perceived as independent, timid, introverted, restrained, boring, or alternatively, a higher value range may be provided if the content is perceived as engaging, seeking attention, assertive, sociable. Agreeableness is evaluated as the tendency to be compassionate and cooperative towards others. This trait may be provided a value range if the presentation is perceived as selfish, uncaring, uncooperative, confrontational or arrogant, or alternatively, a higher value range may be provided if the content is perceived as caring, sympathetic, cooperative, or trustworthy. The emotional range is evaluated as the tendency to be sensitive to the environment. This trait may be provided a value range if the presentation is perceived as calm, bland, content, relaxed or alternatively a higher value range may be provided if the content is perceived as concerned, frustrated angry, passionate, upset, stressed or impulsive. These tones, descriptions, and weights are merely illustrative and additional tones, descriptions or weights may be utilized.

Language tones may be analyzed to measure the user's writing style. The various styles may include analytic, confidence and tentative. The analytic style may focus on the individual's reasoning and analytical attitude about things. The analytic style may be provided a value range if the text contains little or no evidence of analytical tone or alternatively a higher value range if the presentation is more likely to be perceived as intellectual, rational, systematic, emotionless, or impersonal. The confidence style may focus on the presenter's degree of certainty. The confidence style may be provided a value range if the text contains little or no evidence of confidence in tone or alternatively a higher value range if the style is more likely to be perceived as assured, collected, hopeful or egotistical. The tentative style may focus on the presenter's degree of inhibition. The tentative style may be provided a lower value range if the text contains little or no evidence of tentativeness in tone or a higher value range if the style is more likely to be perceived as questionable, doubtful limited, or debatable. The word stemming and summation module. The sentiment analysis can be executed in the server406in the processing module412or in the user device402. In other embodiments of the invention, the user emotion information can be provided to a server406over a network.

In addition, the server406includes a plurality of modules410-416to perform the techniques described herein. The server406includes a transmitter/receiver (TX/RX) module410that is configured to communicate with one or more user devices402over a network408and one or more external components. For example, the server406is configured to receive user information including emotional information and behavior information and the server is configured to transmit data such as a particular advertisement. The server406also includes a processing module412that is configured to perform a number functions that include monitoring user information and processing the user information to determine a pattern in the user's interactions with a computing device and determine a pattern in the user's responses to an advertisement.

In one or more embodiments of the invention, a user pattern is determined over a period of time and mapped to a detected emotion. The period of time can be configurable by a user and can be a period such as a day, week, month, etc. The user pattern of interactions can track how a user interacts with different websites or applications. For example, a sequence of applications that a user routinely visits or uses to make purchases or other data indicating how the user manipulates the user device402.

In one or more embodiments of the invention, the server406is configured to determine a pattern in the user responses to particular advertisements or types of advertisements during a determined emotional state. For example, responsive to receiving an advertisement the server406can determine whether the user has made a purchase or followed a particular link associated with the advertisement. These user patterns can be mapped to the user's emotion.

In one or more embodiments of the invention, server406can also store user information in a user profile418related to the channels that a user receives the advertisements. The channel information can include devices such as a mobile phone, laptop computer, tablet, or another type of device. A device ID can be used to identify the channel such as an IP address, MAC address, device ID, etc. The channel information can also indicate the type of user device that a user is likely to interact with. For example, a user may be likely to view advertisements on a desktop computer instead of on a mobile phone due to the limited battery restriction. In addition, channel information can also include information pertaining to applications that are frequently visited by a user such as a social media application, email application, shopping website, etc. The applications can be associated with an application ID such as a URL and stored in a user profile418. It should be understood that other types of application IDs can be used to monitor the user's behavior. The channel information can also identify various sources that provide advertisements to the user such as advertisements provided on specific social media platforms, streaming services, emails, retailers, etc. A history can be stored in the user profile418to determine the user's typical interactions. In one or more embodiments of the invention, the user history can be mapped to the emotional state of the user.

The system400is also configured to monitor the manner in which the advertisement is delivered to the user. In one or more embodiments of the invention, the system400can determine if one channel of providing the advertisement is more successful than others. For example, it can be determined whether a pop-up advertisement or an advertisement provided in a banner is on a web page is selected. The success of an advertisement can be determined according to a user making a purchase based on the received advertisement or a user watching a complete video advertisement instead of skipping over it. It should be understood that other techniques can be used to determine the effectiveness of the advertisement.

In one or more embodiments of the invention, one or more pieces of information can be used to generate a profile that can be used to select an advertisement for a user. The selection of an advertisement by an ad selection module414can be based on the user's behavior during a particular mood and the user's reactions to advertisements during the particular mood. In one or more embodiments of the invention, the user profile418can be updated at a configurable interval. In addition, the update can occur according to a user input and the detection of an event.

In one or more embodiments of the invention, a user can set their preferences for targeted advertisements. In other embodiments of the invention, the user's preferences can be monitored and determined over a period of time. In one or more embodiments of the invention, a default setting can be used to select advertisements by the ad selection module414. In one example, the default settings can be based on segments such as an age range 20-25, 26-34, 35+, etc. until a threshold level of data related to the user has been collected. The threshold can indicate a period of time or a threshold number of samples to generate a user profile. A user's age can be obtained from a user profile of a social network, an email profile, device profile, or any other location that stores. Each should be understood that segments can be based on other factors such as common interests in sports, clubs, schools, etc.

A user profile418can store the mood, pattern of user behavior, and pattern of responding to advertisements. The user profile418can also include advertisement type information indicating categories including but not limited to sports, education, news, shopping, services, etc. In addition, the advertisements can also include subcategories within each of the categories. For example, the sports category of advertisements can include basketball, football, baseball, and the like, where the advertisements are selected based on the user's current mood.

In an example, the system400determines user patterns of behavior when a user is in a particular mood. For example, a user when determined to be in a happy mood by a device, a user's interactions are monitored to determine a pattern in the user's interactions. The pattern can include online shopping on a mobile device during a happy mood. The pattern can include additional information such as determining a user attends local sporting events. The additional information can include team information, location information, win/loss information, etc. In the event the user's preferred team wins, the user is likely to be in a happy mood and purchase team-related merchandise. In the event the user's preferred team loses, the user may have a habit of not purchasing team gear but searching social media.

The system400also determines a pattern of reactions to the advertisements or other content when the users are in a particular mood. For example, the system400can determine when the user's team wins, advertisements that are targeted to the user related to the user's team result in a purchase. On the other hand, the system400can determine when the user's team loses, the advertisements that are related to the user's team do not result in a purchase.

Individuals may have different behaviors according to specific moods. In a non-limiting example, when John is sad he may watch sports to cheer him up. However, when Jane is sad, she may like to shop online. Even further, when Joe is sad, he may like to buy luxury items. The emotional state of each user is sad, but each person has a different behavior/pattern behind their emotional state. John may only click on advertisements on the side of his web page, while Jane is more likely to interact with pop-ups while she is surfing the website she is purchasing.

Now referring toFIG. 5, a flowchart of a method500for delivering advertisements based on user sentiment and learned behavior in accordance with one or more embodiments of the invention. The method500can be implemented in any of the systems shown inFIGS. 1-4. The method500begins at block502and proceeds to block504which provides for collecting emotional information of a user. At block506, the method500provides for monitoring user interactions based at least in part on the collected emotional information of the user. Block508provides for determining a pattern of user interactions with one or more applications of a user device based on the emotional information. Block510provides for determining a pattern of user responses to content based on the emotional information. The method500at block512provides for generating a profile based at least in part on the pattern of user interactions and the pattern of user responses. In one or more embodiments of the invention, a default profile can be used to select content for the user until a threshold limit of data is collected. The method500ends at block514.

Referring now toFIG. 6, a computer program product600in accordance with an embodiment that includes a computer-readable storage medium602and program instructions604is generally shown.

The techniques described herein improve over the prior art by detecting and leveraging a user's emotional state and user's typical behavior of interaction with applications during the detected emotional state to select advertisements to provide to the user.

One or more embodiments of the invention improve over the prior art by increasing the likelihood of the effectiveness of the selection of a targeted advertisement and by also increasing a user experience by factoring the user emotion. Once a pattern or behavior is determined, the system can use this information to better determine the most appropriate vehicle to push advertisements to those users at particular moments.

In a non-limiting example, there can be multiple advertisements that a user might be interested in. These advertisements can be determined using existing techniques, such as analyzing the browsing history, email content, etc. In some cases, these advertisements are ranked according to the effectiveness of the advertisement, where the effectiveness is based on the likelihood the advertisements will clicked by a user or will result in a purchase. The effectiveness of specific advertisements can be used in conjunction with the user's emotional state to select advertisements for the user. In some embodiments of the invention, when the user's emotional state and behavior pattern indicate a high likelihood of the user making a purchase, the most effective advertisement can be selected and presented to the user. This will further maximize the conversion rate.

In another non-limiting example, the advertising system can provide multiple level of services to an advertiser based on the likelihood of a user making a purchase. The different level of services can be identified using the emotional state and user behavior. The level of services can include a “high level” which indicates high confidence that a user will make a purchase for the specific product; a “medium level” which indicates a medium confidence that a user will make a purchase for the specific product; or a “low level” which indicates a low confidence that a user will make a purchase for the specific product. In a different non-limiting example, a specific advertisement can be provided when a particular program is provided and a particular mood is detected such as presenting the advertisement when the user is in happy mood after a sports game”. It should be understood that any number of service levels can be used to provide different levels of granularity to the advertisement system.

The techniques described herein provide a system and method for determining how an advertisement will be received based on specific emotions. In addition, techniques described herein provide for an analysis of an individual's emotions, recognize patterns/behaviors and will determine the best source for advertisements based on the associated emotion.