Modeling analysis of team behavior and communication
A computer evaluates free-form text messages among members of a team, using natural language processing techniques to process the text messages and to assess psychological state of the team members as reflected it the text messages. The computer assembles the psychological state as reflected in the messages to evaluate team collective psychological state. The computer reports a trend of team collective psychological state in natural language text form.
1. A method, comprising the steps of:
by computer, evaluating free-form text messages among team members of a team, using natural language processing techniques to process the text messages and to assess psychological states of the team members as reflected in the text messages;
by computer, assembling a psychological state of each of the team members as reflected in the text messages to evaluate team collective psychological state;
in the memory of a computer, modeling the team with a multiplex graph to obtain the team collective psychological state, wherein nodes of the multiplex graph correspond to team members, layers of the multiplex graph correspond to respective psychological attributes of the assembled psychological states of the team members, and edges of the multiplex graph within each layer represent the respective psychological attributes as reflected between the team members corresponding to the nodes of the edges; and
by computer, reporting a trend of the team collective psychological state in natural language text form.
2. The method of claim 1 , further comprising the step of:
using natural language processing techniques to process the text messages and to assess at least three emotions of the team members as reflected in the text messages, the three emotions drawn from the group consisting of anger, disgust, fear, happiness, sadness, love, surprise, trust, distrust, surprise, or anticipation.
3. The method of claim 1 , further comprising the step of:
using natural language processing techniques to process the text messages and to assess sentiment of the team members as reflected in the text messages, sentiment lying on a spectrum from positive to neutral to negative.
4. The method of claim 1 , further comprising the step of:
monitoring one or more environmental factors in a work area of the team, at least one of the monitored environmental factors being drawn from a group consisting of temperature and humidity.
5. The method of claim 1 , further comprising the step of:
in the memory of a computer, histogramming psychological state assessment counts of each of the team members by time interval.
6. The method of claim 5 , further comprising the step of:
in the memory of a computer, histogramming psychological state assessment counts of each of the team members by time interval and psychological assessment value.
7. The method of claim 1 , further comprising the step of:
in the memory of a computer, computing a trend of the team collective psychological state and assessment counts by time interval.
8. Apparatus, comprising:
a computer processor;
a nontransitory memory, the nontransitory memory having stored therein one or more programs to cause the processor to:
evaluate free-form text messages among team members of a team, using natural language processing techniques to process the text messages and to assess psychological state of the team members as reflected in the text messages;
assemble the psychological state of each of the team members as reflected in the text messages to evaluate team collective psychological state;
model the team with a multiplex graph to obtain the team collective psychological state, wherein nodes of the multiplex graph correspond to team members, layers of the multiplex graph correspond to respective psychological attributes of the assembled psychological states of the team members, and edges of the multiplex graph within each layer represent the respective psychological attributes as reflected between the team members corresponding to the nodes of the edges; and
report a trend of the team collective psychological state in natural language text form.
9. The apparatus of claim 8 , the programs being further programmed to cause the processor to:
using natural language processing techniques to process the text messages and to assess at least three emotions of the team members as reflected in the text messages, the three emotions drawn from the group consisting of anger, disgust, fear, happiness, sadness, love, surprise, trust, distrust, surprise, or anticipation.
10. The apparatus of claim 8 , the programs being further programmed to cause the processor to:
using natural language processing techniques to process the text messages and to assess sentiment of the team members as reflected in the text messages, sentiment lying on a spectrum from positive to neutral to negative.
11. The apparatus of claim 8 , further comprising the step of:
monitoring one or more environmental factors in a work area of the team, at least one of the monitored environmental factors being drawn from a group consisting of temperature, humidity, sound level, and sound character.
12. The apparatus of claim 8 , the programs being further programmed to cause the processor to:
in the memory of a computer, histogramming psychological state assessment counts by time interval.
13. The apparatus of claim 12 , the programs being further programmed to cause the processor to:
in the memory of a computer, histogramming psychological state assessment counts by time interval and psychological assessment value.
14. The apparatus of claim 8 , the programs being further programmed to cause the processor to:
in the memory of a computer, computing a trend of the team collective psychological state and assessment counts by time interval.
15. The method of claim 1 , wherein each of the nodes comprises a characteristic vector of individual linguistic signals obtained from the evaluating of the free-form text messages.
16. The method of claim 1 , wherein each edge of the multiplex graph captures a separate relationship between pairs of team members.
17. The method of claim 1 , wherein each edge of the multiplex graph comprises a model of a dyadic pair of team members.
18. The method of claim 17 , wherein each edge of the multiplex graph is annotated by characteristics of interactions between the dyadic pair.
19. The method of claim 1 , further comprising:
determining measures of graph connectedness between nodes to assess overall level of intrateam communication.
20. The method of claim 1 , further comprising:
determining measures of graph centrality to determine putative influencers in the team.