Mining Semi-Structured Social Media

• US 20140136541A1
• Filed: 11/15/2012
• Published: 05/15/2014
• Est. Priority Date: 11/15/2012
• Status: Active Grant

• Current Assignee: Adobe Inc.
• Original Assignee: Adobe Systems Incorporated (Adobe Inc.)
• Inventors: Farahat, Ayman Omar, Ahmed, Nesreen Kamel Abuseree
• Granted Patent: US 9,002,852 B2
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking

Abstract

Methods and apparatus for analysis of semi-structured social media are described. A method comprises classifying a plurality of user-generated content entries into a plurality of categories based at least in part on an analysis of respective structured components of at least a subset of the plurality of entries. The method further includes determining, based at least in part on an analysis of additional components of entries of a particular category, a set of representative content elements of entries of the particular category, and generating a report that comprises one or more representative content elements of the particular category.

Claims

1. A method, comprising:

performing, by one or more computing devices;

classifying a plurality of user-generated content entries into a plurality of categories based at least in part on an analysis of respective structured components of at least a subset of the plurality of entries;

determining, based at least in part on an analysis of additional components of entries of a particular category, a set of representative user-generated content elements of the entries of the particular category; and

generating a report that comprises one or more representative user-generated content elements of the set of representative user-generated content elements of the entries of the particular category.

2. The method as recited in claim 1, wherein the additional components comprise natural language text.

3. The method as recited in claim 1, further comprising:

collecting the plurality of user-generated content entries using at least one of;

(a) a screen-scraping technique on a web page, (b) one or more invocations of an application programming interface (API), or (c) one or more database queries.

4. The method as recited in claim 1, wherein the structured component of a particular entry of the plurality of user-generated content entries comprises an indication of at least one of:

(a) a user-selected rating level, (b) a date, (c) a geographical location, (d) a purchase price, or (e) a demographic category of a user.

5. The method as recited in claim 1, wherein the plurality of categories include at least one of:

(a) a positive rating category, (b) a negative rating category, or (c) a neutral rating category.

6. The method as recited in claim 1, wherein the analysis of an additional component of a particular entry of the particular category comprises an n-gram analysis of user-generated text.

7. The method as recited in claim 1, wherein the analysis of an additional component of a particular entry of the particular category comprises a semantic analysis of user text to identify parts of speech.

8. The method as recited in claim 1, wherein the set of representative user-generated content elements of the entries of the particular category comprises a set of frequent word combinations, wherein the report comprises an indication of a context in which a variant of a frequent word combination of the set of frequent word combinations occurred in a particular user-generated content entry.

9. The method as recited in claim 1, wherein the set of representative user-generated content elements of the entries of the particular category comprises a set of frequent word combinations, further comprising:

generating, based at least in part on the report, a survey question in requesting additional feedback associated with a particular frequent word combination of the set of frequent word combinations.

10. A system, comprising:

one or more processors; and

a memory comprising program instructions executable by the one or more processors to;

classify a plurality of user-generated content entries into a plurality of categories based at least in part on an analysis of respective structured components of at least a subset of the plurality of entries;

determine, based at least in part on an analysis of additional components of entries of a particular category, a set of representative user-generated content elements of the entries of the particular category; and

generate a report that comprises one or more representative user-generated content elements of the set of representative user-generated content elements of the entries of the particular category.

11. The system as recited in claim 10, wherein the instructions are executable by the one or more processors to:

obtain the plurality of user-generated content entries using at least one of;

(a) a screen-scraping technique on a web page, (b) one or more invocations of an application programming interface (API), or (c) one or more database queries.

12. The system as recited in claim 10, wherein an additional component of a particular entry of the particular category comprises natural language text, wherein the analysis of the additional component comprises an n-gram analysis of user-generated text.

13. The system as recited in claim 10, wherein the analysis of an additional component of a particular entry of the particular category comprises a semantic analysis of user-generated text to identify phrases corresponding to parts of speech of a language.

14. The system as recited in claim 10, wherein the set of representative user-generated content elements of the entries of the particular category comprises a set of frequent word combinations, wherein the report comprises a link to one or more contexts in which a variant of a particular frequent word combination of the set of frequent word combinations occurred in one or more user-generated content entries of the plurality of user-generated content entries.

15. A non-transitory computer-readable storage medium storing program instructions that when executed by a computing device implement:

classifying a plurality of user-generated content entries into a plurality of categories based at least in part on an analysis of respective structured components of at least a subset of the plurality of entries;

determining, based at least in part on an analysis of additional components of entries of a particular category, a set of representative user-generated content elements of the entries of the particular category; and

generating a report that comprises one or more representative user-generated content elements of the set of representative user-generated content elements of the entries of the particular category.

16. The non-transitory computer-readable storage medium as recited in claim 15, wherein the program instructions are computer-executable to implement:

collecting the plurality of user-generated content entries using at least one of;

(a) a screen-scraping technique on a web page, (b) one or more invocations of an application programming interface (API), or (c) one or more database queries.

17. The non-transitory computer-readable storage medium as recited in claim 15, wherein the structured component of a particular entry of the plurality of user-generated content entries comprises an indication of at least one of:

(a) a user-selected rating level, (b) a date, (c) a geographical location, (d) a purchase price, or (e) a demographical category of a user.

18. The non-transitory computer-readable storage medium as recited in claim 15, wherein an additional component of a particular entry of the particular category comprises user-generated text, wherein the analysis of the additional component comprises a use of one or more of:

tokenization, word-stemming, stop-word removal, n-gram computation, part-of-speech tagging, and chunking.

19. The non-transitory computer-readable storage medium as recited in claim 15, wherein the analysis of an additional component of a particular entry of the particular category comprises a semantic analysis.

20. The non-transitory computer-readable storage medium as recited in claim 15, wherein the plurality of categories comprise a first category and a second category, wherein the respective sets of representative user-generated content elements of the entries of the first category comprise respective sets of frequent word combinations, and wherein the report comprises an indication of a particular frequent word combination identified in the first category that does not appear in a set of frequent word combinations for the second category.