Compared sentiment queues
Tracking social collaboration messages includes setting, by a computer, for a discussion group, a respective quota for each of a plurality of sentiment types assignable to a textual message; monitoring, by the computer, typing, by a sender, of text of a message for the discussion group; based on the monitored typing, determining, by the computer, a sentiment type of the message, the determined sentiment type being one of the plurality of sentiment types; and providing, by the computer, to the sender, recommendation for editing the message based on the quota set for the determined sentiment type.
1. A computer-implemented method within and by a sentiment analysis and queue control system, comprising:
identifying, within a message queue for a collaboration platform, an original electronic message created by a sender and addressed to a receiver;
performing, using a machine-learning algorithm, a sentiment analysis on the original electronic message within the message queue;
forwarding, to a user interface within a client device associated with the sender, a result of the sentiment analysis;
delaying, for a period of time, delivery of the original electronic message to the receiver based upon a sentiment type of the original electronic message causing a sentiment quota associated with the receiver to exceed a predetermined value corresponding to the receiver; and
delivering, after the period of time, the original electronic message to the receiver based upon the sentiment type not causing the sentiment quota of the receiver to be exceeded, wherein
the sentiment analysis determines the sentiment type of the original electronic message, and
the sentiment quota associated with the receiver changes based upon the sentiment type of the original electronic message.
2. The method of claim 1 , wherein
the forwarding the result of the sentiment analysis includes an electronic recommendation for editing the original electronic message based upon the sentiment type causing the sentiment quota of the receiver to be exceeded.
3. The method of claim 1 , wherein
the message queue is located within a server associated with the collaboration platform, and
the original electronic message has already been sent from the sender.
4. The method of claim 1 , wherein
the message queue is located within the client device,
the typing of the sender in generating the original electronic message is real-time monitored, and
the performing the sentiment analysis is performed in real-time.
5. The method of claim 1 , wherein
the user interface is configured to display:
a world-level sentiment analysis of the original electronic message and
a document-level sentiment score.
6. The method of claim 1 , wherein
the predetermined value corresponding to receiver is automatically adjusted based upon biometric data received from a biometric feedback device configured to monitor the receiver.
7. The method of claim 1 , wherein
the sentiment analysis and queue control system manages a plurality of message queues respectively associated with different sentiment types, and
received electronic messages are queued within one of these plurality of message queues based upon a determined sentiment type associated with each of the electronic messages.
8. A computer hardware system including a sentiment analysis and queue control system, comprising:
a hardware processor programmed to initiate the following executable operations:
identifying, within a message queue for a collaboration platform, an original electronic message created by a sender and addressed to a receiver;
performing, using a machine-learning algorithm, a sentiment analysis on the original electronic message within the message queue;
forwarding, to a user interface within a client device associated with the sender, a result of the sentiment analysis;
delaying, for a period of time, delivery of the original electronic message to the receiver based upon a sentiment type of the original electronic message causing a sentiment quota associated with the receiver to exceed a predetermined value corresponding to the receiver; and
delivering, after the period of time, the original electronic message to the receiver based upon the sentiment type not causing the sentiment quota of the receiver to be exceeded, wherein
the sentiment analysis determines the sentiment type of the original electronic message, and
the sentiment quota associated with the receiver changes based upon the sentiment type of the original electronic message.
9. The system of claim 8 , wherein
the forwarding the result of the sentiment analysis includes an electronic recommendation for editing the original electronic message based upon the sentiment type causing the sentiment quota of the receiver to be exceeded.
10. The system of claim 8 , wherein
the message queue is located within a server associated with the collaboration platform, and
the original electronic message has already been sent from the sender.
11. The system of claim 8 , wherein
the message queue is located within the client device,
the typing of the sender in generating the original electronic message is real-time monitored, and
the performing the sentiment analysis is performed in real-time.
12. The system of claim 8 , wherein
the user interface is configured to display:
a world-level sentiment analysis of the original electronic message and
a document-level sentiment score.
13. The system of claim 8 , wherein
the predetermined value corresponding to receiver is automatically adjusted based upon biometric data received from a biometric feedback device configured to monitor the receiver.
14. The system of claim 8 , wherein
the sentiment analysis and queue control system manages a plurality of message queues respectively associated with different sentiment types, and
received electronic messages are queued within one of these plurality of message queues based upon a determined sentiment type associated with each of the electronic messages.
15. A computer program product, comprising:
a computer readable storage medium having program code stored thereon,
the program code, which when executed by a computer hardware system including a sentiment analysis and queue control system, causes the computer hardware system to perform:
identifying, within a message queue for a collaboration platform, an original electronic message created by a sender and addressed to a receiver;
performing, using a machine-learning algorithm, a sentiment analysis on the original electronic message within the message queue;
forwarding, to a user interface within a client device associated with the sender, a result of the sentiment analysis;
delaying, for a period of time, delivery of the original electronic message to the receiver based upon a sentiment type of the original electronic message causing a sentiment quota associated with the receiver to exceed a predetermined value corresponding to the receiver; and
delivering, after the period of time, the original electronic message to the receiver based upon the sentiment type not causing the sentiment quota of the receiver to be exceeded, wherein
the sentiment analysis determines the sentiment type of the original electronic message, and
the sentiment quota associated with the receiver changes based upon the sentiment type of the original electronic message.
16. The computer program product of claim 15 , wherein
the forwarding the result of the sentiment analysis includes an electronic recommendation for editing the original electronic message based upon the sentiment type causing the sentiment quota of the receiver to be exceeded.
17. The computer program product of claim 15 , wherein
the sentiment analysis and queue control system manages a plurality of message queues respectively associated with different sentiment types,
received electronic messages are queued within one of these plurality of message queues based upon a determined sentiment type associated with each of the electronic messages,
the plurality of message queues are located within a server associated with the collaboration platform, and
the original electronic message has already been sent from the sender.
18. The computer program product of claim 15 , wherein
the message queue is located within the client device,
the typing of the sender in generating the original electronic message is real-time monitored, and
the performing the sentiment analysis is performed in real-time.
19. The computer program product of claim 15 , wherein
the user interface is configured to display:
a world-level sentiment analysis of the original electronic message and
a document-level sentiment score.
20. The computer program product of claim 15 , wherein
the predetermined value corresponding to receiver is automatically adjusted based upon biometric data received from a biometric feedback device configured to monitor the receiver.