Emotion processing systems and methods
A system for conducting parallelization of tasks is disclosed. The system includes an interface for receiving messages comprising a representation of logic describing two tasks to be executed in parallel, the message further comprising a content payload for use in the tasks. The system further includes a processor comprising devices running on independent machines, each device comprising a processing manager unit and at least two processing units. The processing manager is configured to parse the received messages and to distribute the at least two tasks to the at least two processing units for independent and parallel processing relative to the content payload.
1. A system for conducting parallelization of tasks, comprising:
an interface for receiving messages, each message comprising a representation of logic describing at least two tasks to be executed in parallel, and a content payload to be processed by conducting the tasks; and
a processor comprising a processing manager unit and at least two processing units, wherein the processing manager unit is configured to parse the received messages and to distribute each task to a corresponding processing unit for independent and parallel processing relative to the content payload, wherein each of the at least two tasks are associated with a function to be executed by the processing units;
wherein the processing units are configured to identify the function associated with each task and to recall resources for executing the functions from a plug-in library, wherein the functions comprise an emotional scoring function, wherein the emotional scoring function is a task in parallel with a context determination task, wherein the emotional scoring function analyzes textual data and classifies grams in the textual data into categories.
2. The system of claim 1 , further comprising:
a messaging source having a website crawler, the website crawler configured to generate the message having the representation of logic describing parallel tasks and the content payload for use in the parallel tasks.
3. The system of claim 1 , further comprising:
a messaging source having a streaming data interface for receiving streaming data, wherein the messaging source is configured to process the streaming data and to generate the messages using the streaming data.
4. The system of claim 3 , wherein the messaging source is configured to use the streaming data to create the content payload and wherein identification of the function associated with the tasks is not a part of the streaming data.
5. The system of claim 1 , further comprising:
a messaging source comprising a query engine for querying a data source and for generating a series of the messages using query results, wherein identification of the function associated with the tasks is not a part of the query results.
6. The system of claim 1 , wherein the interface comprises a framework manager configured to queue the messages, and wherein each processing manager unit is configured to request new messages from a queue when resources permit.
7. The system of claim 1 , wherein the messages are not compiled and do not include the source code for the tasks, and wherein the messages utilize a mark-up language to identify the parallel tasks and the content payload.
8. The system of claim 7 , wherein the content payload comprises a reference to data not embedded within the messages.
9. The system of claim 8 , wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.
10. The system of claim 1 , wherein the function comprises a first emotional scoring function for a first emotion and another task includes a second emotional scoring function for a second emotion, wherein both of the first and second emotional scoring functions are executed as parallel tasks and represented as parallel tasks in the logic of the message.
11. A computerized method for scoring documents using rule-based criteria, comprising:
parsing textual data in a document to identify one or more grams in the document, wherein the grams are extracted from the document;
categorizing the grams into a plurality of categories as a first task, wherein the categorizing is performed by a first processing unit executing in parallel with a second processing unit executing a second task that is different from the first task, the categories representing a level of emotion;
quantifying a number of grams in the categories;
using a rule-based engine to assign a construct score to the document based on the number of grams in each category for emotional scoring, wherein assigning the construct score comprises applying scoring rules to the quantities of the grams to determine the construct score for the document,
wherein the construct score is calculated according to the following equation
W
=
{
1
0
if
E
>
0
,
LG
=
0
9
if
G
>
2
,
LG
=
0
8
if
G
>
1
,
LG
=
0
where W is the construct score, and E is the number of grams in an excellent category, G is the number of grams in the good category, and LG is the number of grams in a less than good category, wherein the less than good category is a combined category.
12. The method of claim 11 , further comprising:
analyzing textual data in a document to identify the one or more grams present in the textual data.
13. The method of claim 12 wherein the categories comprise Excellent, Good, Fair, Questionable, Bad, and Terrible.
14. The method of claim 13 further comprising combining two or more of the categories and summing the grams in a combination of categories.
15. The method of claim 11 , wherein the construct score is on a scoring scale from 0-10 where a score of 10 indicates clear positive evidence of the construct and a score of 0 indicates clear negative evidence of the construct.
16. The method of claim 11 , further comprising calculating an impactfulness starting score based on the construct score.
17. The method of claim 16 wherein the impactfulness starting score is calculated according to the following equation:
WM
0
=
{
10
if
1
0
≥
CX
>
5
0
if
0
≤
CX
<
5
5
otherwise
where WM 0 is the impactfulness starting score and CX is the construct score.
18. A computerized method for scoring documents using rule-based criteria, comprising:
parsing textual data in a document to identify one or more grams in the document, wherein the grams are extracted from the document;
categorizing the grams into a plurality of categories as a first task, wherein the categorizing is performed by a first processing unit executing in parallel with a second processing unit executing a second task that is different from the first task, the categories representing a level of emotion;
quantifying a number of grams in the categories;
using a rule-based engine to assign a construct score to the document based on the number of grams in each category for emotional scoring, wherein assigning the construct score comprises applying scoring rules to the quantities of the grams to determine the construct score for the document; and
calculating an impactfulness starting score based on the construct score, wherein the impactfulness starting score is determined by assigning a maximum score to documents with a positive construct score and assigning a minimum score to documents with a negative construct score.
19. The method of claim 18 , wherein the construct score is calculated according to the following equation
W
=
{
1
0
if
E
>
0
,
LG
=
0
9
if
G
>
2
,
LG
=
0
8
if
G
>
1
,
LG
=
0
where W is the construct score, and E is the number of grams in an excellent category, G is the number of grams in the good category, and LG is the number of grams in a less than good category, wherein the less than good category is a combined category.
20. The method of claim 18 wherein the impactfulness starting score is calculated according to the following equation:
WM
0
=
{
10
if
1
0
≥
CX
>
5
0
if
0
≤
CX
<
5
5
otherwise
where WM 0 is the impactfulness starting score and CX is the construct score.
21. The method of claim 18 , further comprising:
analyzing textual data in a document to identify the one or more grams present in the textual data.
22. The method of claim 21 wherein the categories comprise Excellent, Good, Fair, Questionable, Bad, and Terrible.
23. The method of claim 22 further comprising combining two or more of the categories and summing the grams in a combination of categories.
24. The method of claim 18 , wherein the construct score is on a scoring scale from 0-10 where a score of 10 indicates clear positive evidence of the construct and a score of 0 indicates clear negative evidence of the construct.