Systems and methods to quantify consumer sentiment based on transaction data
First Claim
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1. A computer-implemented method, comprising:
- providing a computing apparatus havinga transaction handler configured in an electronic payment processing network connecting separate computers, includingfirst computers controlling consumer accounts from which payments of transactions are made in the electronic payment processing network;
second computers controlling merchant accounts in which the payments are received via the electronic payment processing network; and
transaction terminals configured to initiate the transactions in the electronic payment processing network using identifications of the consumer accounts;
a data warehouse coupled with the transaction handler and storing;
first transaction data recording payment transactions processed by the transaction handler in a first period of time, andsecond transaction data recording payment transactions processed by the transaction handler in a second period of time; and
a portal coupled with the data warehouse;
receiving, in the portal of the computing apparatus using a communication channel outside the electronic payment processing network, internet content indicative of consumer sentiment during the first period of time;
evaluating, by the computing apparatus, consumer sentiment values based on the internet content published during the first period of time;
correlating, by the computing apparatus using a machine learning technique, the first transaction data recording the payment transactions processed by the transaction handler in the first period of time with the consumer sentiment values evaluated based on the internet content published during the first period of time;
generating, by the computing apparatus, a computer quantification model of consumer sentiment from the correlating;
training the computer quantification model based on adjusting parameters of the quantification model to reduce differences betweenthe consumer sentiment values evaluated based on internet content published during the first period of time, andnumerical values computed from applying the first transaction data in the first period of time to the quantification model;
adjusting regional and temporal differences in emotional sentiment for the numerical values in the training of the quantification model;
receiving, by the computing apparatus from the data warehouse, the second transaction data recording payment transactions of a group of users during the second period of time;
applying, by the computing apparatus, the second transaction data to the quantification model;
determining, by the computing apparatus through the applying of the second transaction data to the computer quantification model, a numerical value of consumer sentiment of the group of the users during the second period of time;
identifying, by the computing apparatus, offers based at least in part on the numerical value of consumer sentiment of the group of the users; and
communicating, by the portal using a channel outside the electronic payment processing network, the offers to the group of the users.
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Abstract
A computing apparatus is configured to quantify consumer sentiment at an aggregated or micro level using transaction data that records the transactions processed by a transaction handler of a payment system. A quantification model is generated based on correlating transaction data with respective emotional content indices extracted from data sources, such as regional news, weather, stock markets, movie themes, local sports, employment, traffic conditions, etc. Using the quantification model, consumer sentiment can be evaluated at various granularity levels, based on the granularity of the user group and the time period of the transaction data used in the quantification model.
33 Citations
16 Claims
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1. A computer-implemented method, comprising:
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providing a computing apparatus having a transaction handler configured in an electronic payment processing network connecting separate computers, including first computers controlling consumer accounts from which payments of transactions are made in the electronic payment processing network; second computers controlling merchant accounts in which the payments are received via the electronic payment processing network; and transaction terminals configured to initiate the transactions in the electronic payment processing network using identifications of the consumer accounts; a data warehouse coupled with the transaction handler and storing; first transaction data recording payment transactions processed by the transaction handler in a first period of time, and second transaction data recording payment transactions processed by the transaction handler in a second period of time; and a portal coupled with the data warehouse; receiving, in the portal of the computing apparatus using a communication channel outside the electronic payment processing network, internet content indicative of consumer sentiment during the first period of time; evaluating, by the computing apparatus, consumer sentiment values based on the internet content published during the first period of time; correlating, by the computing apparatus using a machine learning technique, the first transaction data recording the payment transactions processed by the transaction handler in the first period of time with the consumer sentiment values evaluated based on the internet content published during the first period of time; generating, by the computing apparatus, a computer quantification model of consumer sentiment from the correlating; training the computer quantification model based on adjusting parameters of the quantification model to reduce differences between the consumer sentiment values evaluated based on internet content published during the first period of time, and numerical values computed from applying the first transaction data in the first period of time to the quantification model; adjusting regional and temporal differences in emotional sentiment for the numerical values in the training of the quantification model; receiving, by the computing apparatus from the data warehouse, the second transaction data recording payment transactions of a group of users during the second period of time; applying, by the computing apparatus, the second transaction data to the quantification model; determining, by the computing apparatus through the applying of the second transaction data to the computer quantification model, a numerical value of consumer sentiment of the group of the users during the second period of time; identifying, by the computing apparatus, offers based at least in part on the numerical value of consumer sentiment of the group of the users; and communicating, by the portal using a channel outside the electronic payment processing network, the offers to the group of the users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A tangible, non-transitory computer-storage medium storing instructions configured to instruct a computing apparatus to perform a method, the method comprising:
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receiving, in a portal of the computing apparatus using a communication channel outside an electronic payment processing network, internet content indicative of consumer sentiment during a first period of time, wherein the computing apparatus including a transaction handler configured in the electronic payment processing network connecting separate computers, including first computers controlling consumer accounts from which payments of transactions are made in the electronic payment processing network; second computers controlling merchant accounts in which the payments are received via the electronic payment processing network; and transaction terminals configured to initiate the transactions in the electronic payment processing network using identifications of the consumer accounts; a data warehouse coupled with the transaction handler and storing; first transaction data recording payment transactions processed by the transaction handler in a first period of time, and second transaction data recording payment transactions processed by the transaction handler in a second period of time; and a portal coupled with the data warehouse; evaluating, by the computing apparatus, consumer sentiment values based on the internet content published during the first period of time; correlating, by the computing apparatus using a machine learning technique, the first transaction data recording the payment transactions processed by the transaction handler in the first period of time with the consumer sentiment values evaluated based on the internet content published during the first period of time; generating, by the computing apparatus, a computer quantification model of consumer sentiment from the correlating; training the computer quantification model based on adjusting parameters of the quantification model to reduce differences between the consumer sentiment values evaluated based on internet content published during the first period of time, and numerical values computed from applying the first transaction data in the first period of time to the quantification model; adjusting regional and temporal differences in emotional sentiment for the numerical values in the training of the quantification model; receiving, by the computing apparatus from the data warehouse, the second transaction data recording payment transactions of a group of users during the second period of time; applying, by the computing apparatus, the second transaction data to the quantification model; determining, by the computing apparatus through the applying of the second transaction data to the computer quantification model, a numerical value of consumer sentiment of the group of the users during the second period of time; identifying, by the computing apparatus, offers based at least in part on the numerical value of consumer sentiment of the group of the users; and communicating, by the portal using a channel outside the electronic payment processing network, the offers to the group of the users.
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14. A computing apparatus having at least one processor and a memory storing instructions configured to instruct the at least one processor to perform operations, the computing apparatus comprising:
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a transaction handler configured in an electronic payment processing network connecting separate computers, including first computers controlling consumer accounts from which payments of transactions are made in the electronic payment processing network; second computers controlling merchant accounts in which the payments are received via the electronic payment processing network; and transaction terminals configured to initiate the transactions in the electronic payment processing network using identifications of the consumer accounts; a data warehouse coupled with the transaction handler and configured to store first transaction data recording payment transactions processed by the transaction handler in a first period of time, and second transaction data recording payment transactions of a group of users during a second period of time; a portal configured to receive, using a communication channel outside the electronic payment processing network, internet content indicative of consumer sentiment during the first period of time, wherein the computing apparatus is configured to evaluate consumer sentiment values based on the internet content published during the first period of time; a learning engine coupled with the data warehouse and configured to perform correlation, using a machine learning technique, between the payment transactions processed by the transaction handler in the first period of time and the consumer sentiment values evaluated based on the internet content published during the first period of time, generate a computer quantification model of consumer sentiment from the correlation, train the computer quantification model based on adjusting parameters of the quantification model to reduce differences between the consumer sentiment values evaluated based on internet content published during the first period of time, and numerical values computed from applying the first transaction data in the first period of time to the quantification model; adjust regional and temporal differences in emotional sentiment for the numerical values in the training of the quantification model; and a rule engine coupled with the data warehouse and configured to apply the second transaction data to the quantification model, determine, through applying the second transaction data to the computer quantification model, a numerical value of consumer sentiment of the group of the users during the second period of time; and identifying, by the computing apparatus, offers based at least in part on the numerical value of consumer sentiment of the group of the users, wherein the portal is further configured to communicate, using a channel outside the electronic payment processing network, the offers to the group of the users. - View Dependent Claims (15, 16)
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Specification