TIME AND SENTIMENT BASED MESSAGING

• US 20180198749A1
• Filed: 01/10/2017
• Published: 07/12/2018
• Est. Priority Date: 01/10/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: ABOU MAHMOUD, Alaa, BASTIDE, Paul R., LU, Fang
• Granted Patent: US 10,462,095 B2
• Time in Patent Office: Days
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/62 Establishing a time schedule for servicing the requests
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

In time and sentiment based messaging, a server obtains information for a set of messages from online social networks related to a specified object. The information includes at least a user identifier associated with each message, a time of each message, and content of each message. For each unique identifier, the server establishes an initial message from the set of messages related to the specified object, analyzes each message in the set of messages to determine a sentiment of each message, and builds a sentiment line time for each unique user identifier. The server then builds a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier. A targeted message to a set of one or more users may then be generated using the time-based sentiment model.

Claims

1. A method for time and sentiment based messaging, comprising:

obtaining, by a server, information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message;

for each unique identifier, establishing, by the server, an initial message from the set of messages related to the specified object;

analyzing, by the server, each message in the set of messages to determine a sentiment of each message;

building, by the server, a sentiment line time for each unique user identifier;

building, by the server, a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; and

generating a targeted message to a set of one or more users using the time-based sentiment model.

2. The method of claim 1, wherein the establishing of the initial message from the set of messages related to the specified object for each unique identifier comprises:

identifying the unique user identifiers in the set of messages and organizing the set of messages according to the unique user identifiers;

normalizing the set of messages based on the time of each message in the set; and

for each unique user identifier, establishing the initial message.

3. The method of claim 1, wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.

4. The method of claim 1, wherein the generating of the targeted message to the set of one or more users using the time-based sentiment model comprises:

identifying one or more sentiment inflection points in the time-based sentiment model; and

scheduling the targeted message to target a specific sentiment inflection point.

5. The method of claim 4, wherein the specific sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.

6. The method of claim 1, further comprising:

building, by the server, a new sentiment time line for an additional unique user identifier;

updating, by the server, the time-based sentiment model related to the specified object by overlapping the new sentiment time line for the additional unique user identifier according to an initial message for the additional unique user identifier; and

generating a second targeted message to a second set of one or more users using the updated time-based sentiment model.

7. The method of claim 6, wherein the generating of the second targeted message to the second set of the one or more users using the updated time-based sentiment model comprises:

predicting a new sentiment inflection point on the new sentiment time line when sentiments toward the specified object may change; and

scheduling the second targeted messages to target the new sentiment inflection point.

8. A computer program product for time and sentiment based messaging, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

obtain information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message;

for each unique identifier, establish an initial message from the set of messages related to the specified object;

analyze each message in the set of messages to determine a sentiment of each message;

build a sentiment line time for each unique user identifier;

build a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; and

generate a targeted message to a set of one or more users using the time-based sentiment model.

9. The computer program product of claim 8, wherein the establishing of the initial message from the set of messages related to the specified object for each unique identifier comprises:

identify the unique user identifiers in the set of messages and organizing the set of messages according to the unique user identifiers;

normalize the set of messages based on the time of each message in the set; and

for each unique user identifier, establish the initial message.

10. The computer program product of claim 8, wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.

11. The computer program product of claim 8, wherein the generating of the targeted message to the set of one or more users using the time-based sentiment model comprises:

identify one or more sentiment inflection points in the time-based sentiment model; and

schedule the targeted message to target a specific sentiment inflection point.

12. The computer program product of claim 11, wherein the specific sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.

13. The computer program product of claim 8, further comprising:

build a new sentiment time line for an additional unique user identifier;

update the time-based sentiment model related to the specified object by overlapping the new sentiment time line for the additional unique user identifier according to an initial message for the additional unique user identifier; and

generate a second targeted message to a second set of one or more users using the updated time-based sentiment model.

14. The computer program product of claim 13, wherein the generating of the second targeted message to the second set of the one or more users using the updated time-based sentiment model comprises:

predict a new sentiment inflection point on the new sentiment time line when sentiments toward the specified object may change; and

schedule the second targeted messages to target the new sentiment inflection point.

15. A system, comprising:

a processor; and

a computer readable storage medium having program instructions embodied therewith, the program instructions executable by the processor to cause the processor to;

obtain information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message;

for each unique identifier, establish an initial message from the set of messages related to the specified object;

analyze each message in the set of messages to determine a sentiment of each message;

build a sentiment line time for each unique user identifier;

build a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; and

generate a targeted message to a set of one or more users using the time-based sentiment model.

16. The system of claim 15, wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.

17. The system of claim 15, wherein the generating of the targeted message to the set of one or more users using the time-based sentiment model comprises:

identify one or more sentiment inflection points in the time-based sentiment model; and

schedule the targeted message to target a specific sentiment inflection point.

18. The system of claim 17, wherein the specific sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.

19. The system of claim 15, further comprising:

build a new sentiment time line for an additional unique user identifier;

update the time-based sentiment model related to the specified object by overlapping the new sentiment time line for the additional unique user identifier according to an initial message for the additional unique user identifier; and

generate a second targeted message to a second set of one or more users using the updated time-based sentiment model.

20. The system of claim 19, wherein the generating of the second targeted message to the second set of the one or more users using the updated time-based sentiment model comprises:

predict a new sentiment inflection point on the new sentiment time line when sentiments toward the specified object may change; and

schedule the second targeted messages to target the new sentiment inflection point.