The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 26, 2019

Filed:

Feb. 03, 2016
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Rousseau Newaz Kazi, San Francisco, CA (US);

Mark Andrew Rich, Redwood City, CA (US);

Christina Joan Sauper, San Francisco, CA (US);

Amaç Herda{hacek over (g)}delen, Mountain View, CA (US);

Soorya Vamsi Mohan Tanikella, Redmond, WA (US);

Brett Matthew Westervelt, Menlo Park, CA (US);

Maykel Andreas Louisa Jozef Anna Loomans, San Francisco, CA (US);

Adam Eugene Bussing, San Francisco, CA (US);

Shuyi Zheng, Fremont, CA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 7/00 (2006.01); G06F 17/30 (2006.01); G06F 17/27 (2006.01); G06Q 30/02 (2012.01); G06Q 50/00 (2012.01);
U.S. Cl.
CPC ...
G06F 17/30867 (2013.01); G06F 17/2785 (2013.01); G06Q 30/0251 (2013.01); G06Q 50/01 (2013.01);
Abstract

In one embodiment, a method includes accessing a plurality of communications, each communication being associated with a particular content item and including a text of the communication; calculating, for each of the communications, sentiment-scores corresponding to sentiments, wherein each sentiment-score is based on a degree to which n-grams of the text of the communication match sentiment-words associated with the sentiments; determining, for each of the communications, an overall sentiment for the communication based on the calculated sentiment-scores for the communication; calculating sentiment levels for the particular content item corresponding sentiments, each sentiment level being based on a total number of communications determined to have the overall sentiment of the sentiment level; and generating a sentiments-module including sentiment-representations corresponding to overall sentiments having sentiment levels greater than a threshold sentiment level.


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