REAL TIME ORGANIZATION PULSE GATHERING AND ANALYSIS USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

• US 20170193397A1
• Filed: 06/17/2016
• Published: 07/06/2017
• Est. Priority Date: 12/30/2015
• Status: Abandoned Application

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Kottha, Samatha, Rao, Bhavana, Gjadhav, Suraj, Deshmukh, Jayati, Mohamedrasheed, Annervaz Karukapadath, Podder, Sanjay, Sengupta, Shubhashis
• Application Number:
  US15/185,869
• Publication Number:
  US 20170193397A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/58 Use of machine translation, e.g. for multi-lingual retrieval, for server-side translation for client devices or for real-time translation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 3/008 based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods

Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for natural language processing of unstructured text are disclosed. In one aspect, a method includes the actions of receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user. The actions further include parsing the one or more sentences. The actions further include determining one or more classifications of each unstructured data entry. The actions further include determining a sentiment. The actions further include accessing structured data. The actions further include defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data. The actions further include determining sentiments to associate with the group.

Claims

1. A computer-implemented method comprising:

receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user;

for each unstructured data entry, determining whether to translate the one or more sentences in the unstructured data entry to a common language;

for each unstructured data entry, parsing the one or more sentences;

based on the parsed one or more sentences, determining one or more classifications of each unstructured data entry;

for each of the one or more classifications, determining a sentiment;

accessing structured data that is associated with each entity;

defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data;

for each of the one or more groups of users, determining sentiments to associate with the group based on the sentiments associated with the one or more unstructured data entries and based on the entity associated with the respective unstructured data entries;

generating a user interface that includes interface elements for each of the one or more groups and the associated sentiments and classifications; and

providing, for output, the user interface.

2. The method of claim 1, wherein determining a sentiment comprises:

determining the sentiment using one or more of a recursive neural tensor network, a linear support vector machine, a convolutional neural network, a dynamic memory network, or a rule based algorithm.

3. The method of claim 1, comprising:

receiving additional structured data that is associated with an additional user;

based on the additional structured data, identifying, from the one or more groups, a particular group to associate with the additional user; and

determining that the additional user will be associated with the sentiment that is associated with the particular group.

4. The method of claim 1, wherein the structured data comprises demographic data, employment data, and location data.

5. The method of claim 1, wherein:

each of the one or more unstructured data entry includes a time stamp, and

determining sentiments to associate with the group comprises determining sentiment trends to associate with the group.

6. The method of claim 5, comprising:

identifying one or more events that are associated with a respective entity of the structured data; and

determining a relationship between the sentiment trends and the one or more events.

7. The method of claim 1, comprising:

receiving, from an owner of the structured data or from a respective entity, data identifying the one or more classifications.

8. The method of claim 1, comprising:

for each of the one or more classifications, determining a sentiment intensity score,

wherein determining sentiments to associate with the group comprises determining a sentiment intensity score to associate with the group based on the sentiment intensity scores.

9. A system comprising:

one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising;

receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user;

for each unstructured data entry, determining whether to translate the one or more sentences in the unstructured data entry to a common language;

for each unstructured data entry, parsing the one or more sentences;

based on the parsed one or more sentences, determining one or more classifications of each unstructured data entry;

for each of the one or more classifications, determining a sentiment;

accessing structured data that is associated with each entity;

defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data;

for each of the one or more groups of users, determining sentiments to associate with the group based on the sentiments associated with the one or more unstructured data entries and based on the entity associated with the respective unstructured data entries;

generating a user interface that includes interface elements for each of the one or more groups and the associated sentiments and classifications; and

providing, for output, the user interface.

10. The system of claim 9, wherein determining a sentiment comprises:

determining the sentiment using one or more of a recursive neural tensor network, a linear support vector machine, a convolutional neural network, a dynamic memory network, or a rule based algorithm.

11. The system of claim 9, wherein the operations further comprise:

receiving additional structured data that is associated with an additional user;

based on the additional structured data, identifying, from the one or more groups, a particular group to associate with the additional user; and

determining that the additional user will be associated with the sentiment that is associated with the particular group.

12. The system of claim 9, wherein the structured data comprises demographic data, employment data, and location data.

13. The system of claim 9, wherein:

each of the one or more unstructured data entry includes a time stamp, and

determining sentiments to associate with the group comprises determining sentiment trends to associate with the group.

14. The system of claim 13, wherein the operations further comprise:

identifying one or more events that are associated with a respective entity of the structured data; and

determining a relationship between the sentiment trends and the one or more events.

15. The system of claim 9, wherein the operations further comprise:

receiving, from an owner of the structured data or from a respective entity, data identifying the one or more classifications.

16. The system of claim 9, wherein the operations further comprise:

for each of the one or more classifications, determining a sentiment intensity score,

wherein determining sentiments to associate with the group comprises determining a sentiment intensity score to associate with the group based on the sentiment intensity scores.

17. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising:

receiving one or more unstructured data entries that each include one or more sentences, are each associated with an entity, and are each from a user;

for each unstructured data entry, determining whether to translate the one or more sentences in the unstructured data entry to a common language;

for each unstructured data entry, parsing the one or more sentences;

based on the parsed one or more sentences, determining one or more classifications of each unstructured data entry;

for each of the one or more classifications, determining a sentiment;

accessing structured data that is associated with each entity;

defining one or more groups of users based on the structured data, wherein each of the one or more groups shares a common characteristic in the structured data;

for each of the one or more groups of users, determining sentiments to associate with the group based on the sentiments associated with the one or more unstructured data entries and based on the entity associated with the respective unstructured data entries;

generating a user interface that includes interface elements for each of the one or more groups and the associated sentiments and classifications; and

providing, for output, the user interface.

18. The medium of claim 17, wherein determining a sentiment comprises:

determining the sentiment using one or more of a recursive neural tensor network, a linear support vector machine, a convolutional neural network, a dynamic memory network, or a rule based algorithm.

19. The medium of claim 17, wherein the operations further comprise:

receiving additional structured data that is associated with an additional user;

based on the additional structured data, identifying, from the one or more groups, a particular group to associate with the additional user; and

determining that the additional user will be associated with the sentiment that is associated with the particular group.

20. The medium of claim 17, wherein:

each of the one or more unstructured data entry includes a time stamp, and

determining sentiments to associate with the group comprises determining sentiment trends to associate with the group.