SYSTEM FOR GENERATING DATA FROM SOCIAL MEDIA MESSAGES FOR THE REAL-TIME EVALUATION OF PUBLICLY TRADED ASSETS

• US 20120324023A1
• Filed: 03/22/2012
• Published: 12/20/2012
• Est. Priority Date: 03/22/2011
• Status: Active Grant

• Current Assignee: Kubera Logic LLC
• Original Assignee: iSentium, LLC
• Inventors: Di Sciullo, Anna Maria, Karakozoff, Alex, Sastri, Gautham
• Granted Patent: US 9,940,672 B2
• Time in Patent Office: Days
• US Class Current: 709/206
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A system for generating data from social media messages for the real-time evaluation of publicly traded assets includes an ingest component for ingesting the social media messages and a filter module eliminating expressions not considered useful language from social media messages and configuring input social media message into useful formats to form filtered social media messages. The system also includes a language processor processing the filtered social media messages based upon lexical databases to form filter and processed social media messages. The system further includes a sentiment calculator applying rules to the filtered and processed social media messages so as to compute a representation of sentiment values associated with the social media messages. A graphical user interface displaying the sentiment values is also provided.

Claims

1. A system for generating data from social media messages for the real-time evaluation of publicly traded assets, comprising:

an ingest component for ingesting the social media messages;

a filter module eliminating expressions not considered useful language from social media messages and configuring input social media message into useful formats to form filtered social media messages;

a language processor processing the filtered social media messages based upon lexical databases to form filter and processed social media messages;

a sentiment calculator applying rules to the filtered and processed social media messages so as to compute a representation of sentiment values associated with the social media messages; and

a graphical user interface displaying the sentiment values.

2. The system according claim 1, wherein the ingest component acquires social media messages on the basis of keywords or a combination of keywords the ingest component has been programmed to look for.

3. The system according claim 1, further including a language identifier positioned after the ingest component for determining the language used in the social media messages prior to the filter module.

4. The system according claim 1, wherein the filter module eliminates URLs and hash tags from the social media messages.

5. The system according claim 4, wherein the assets are equities or commodities.

6. The system according claim 1, wherein the language processor includes a sentiment-based repository of frequent lexical items.

7. The system according claim 6, wherein the lexical items are associated with polarity values and strength values.

8. The system according claim 7, wherein the polarity values are positive, negative and neutral.

9. The system according claim 7, wherein the strength values associated with the lexical items range from at least 1 to 3, where 1 is the lowest value and 3 is the highest value.

10. The system according claim 6, wherein the language processor includes a stock-based lexical repository composed of lexical items used in the ingested social media messages, as well as lexical items used in stock exchange and financial news wire.

11. The system according claim 10, wherein the lexical items of the stock-based lexical repository are associated with polarity values and strength values and wherein the lexical items of the sentiment-based lexical repository are associated with polarity values and strength values.

12. The system according claim 11, wherein the polarity values of the lexical items of the stock-based lexical repository are positive, negative and neutral and wherein the polarity values of the lexical items of the sentiment-based lexical repository are positive, negative and neutral.

13. The system according claim 11, wherein the strength values associated with the lexical items of the stock-based lexical repository ranges from at least 1 to 3, where 1 is the lowest value and 3 is the highest value and wherein the strength values associated to the lexical items of the sentiment-based lexical repository ranges from at least 1 to 3, where 1 is the lowest value and 3 is the highest value.

14. The system according claim 1, wherein the language processor includes a POS tagger.

15. The system according claim 1, wherein the language processor includes a stock-based lexical repository composed of lexical items, as well as lexical items used in stock exchange and financial news wire.

16. The system according claim 15, wherein the lexical items are associated with polarity values and strength values.

17. The system according claim 16, wherein the polarity values are positive, negative and neutral.

18. The system according claim 1, wherein the language processor includes a parser.

19. The system according claim 1, wherein a sentiment is an integer computed based upon pairs of lexical items in local syntactic context.

22. The system according claim 1, further including an inference engine attempting to provide an answer to a problem, or clarifying uncertainties.

23. The system according claim 24, wherein the inference engine includes a data structure, a knowledge base that uses some knowledge representation structure to capture the knowledge of a specific domain, and a set of inference rules applying to the entities in the relational table and drawing consequences.

24. The system according claim 1, wherein the language processor includes a lemmatizes.

20. The system according claim 20, wherein the sentiment calculator includes means for determining lexical polarity in social media messages.

21. The system according claim 20, wherein the sentiment calculator includes means for determining a strength value of lexical items used in social media messages.