SENTIMENT FEEDBACK

• US 20160071119A1
• Filed: 04/11/2013
• Published: 03/10/2016
• Est. Priority Date: 04/11/2013
• Status: Abandoned Application

• Current Assignee: Longsand Limited (Open Text Corporation)
• Original Assignee: Longsand Limited (Open Text Corporation)
• Inventors: Blanchflower, Sean, Timms, Daniel
• Application Number:
  US14/782,743
• Publication Number:
  US 20160071119A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements

Abstract

Techniques associated with sentiment feedback are described in various implementations. In one example implementation, a method may include generating a proposed sentiment result associated with a document, the proposed sentiment result being generated based on a rule set applied to the document. The method may also include receiving feedback about the proposed sentiment result, the feedback including an actual sentiment associated with the document and a feature of the document that is indicative of the actual sentiment. The method may also include identifying a proposed modification to the rule set based on the feedback.

Claims

1. A computer-implemented method of processing sentiment feedback, the method comprising:

generating, with a computing system, a proposed sentiment result associated with a document, the proposed sentiment result being generated based on a ruleset applied to the document;

receiving, with the computing system, feedback about the proposed sentiment result, the feedback including an actual sentiment associated with the document and a feature of the document that is indicative of the actual sentiment; and

identifying, with the computing system, a proposed modification to the ruleset based on the feedback.

2. The computer-implemented method of claim 1, further comprising causing the proposed modification to the ruleset to be displayed to a user, and applying the proposed modification to the ruleset in response to receiving a confirmation by the user.

3. The computer-implemented method of claim 1, wherein the feature of the document that is indicative of the actual sentiment comprises a portion of content from the document.

4. The computer-implemented method of claim 1, wherein the feature of the document that is indicative of the actual sentiment comprises a classification associated with the document.

5. The computer-implemented method of claim 1, wherein identifying the proposed modification to the ruleset comprises identifying a triggered rule from the ruleset that affects the proposed sentiment result, and generating a proposed change to the triggered rule when the proposed sentiment result does not match the actual sentiment, the proposed change to the triggered rule being generated based on the feature of the document that is indicative of the actual sentiment.

6. The computer-implemented method of claim 5, further comprising causing the triggered rule and the proposed change to the triggered rule to be displayed to a user.

7. The computer-implemented method of claim 1, wherein identifying the proposed modification to the ruleset comprises generating a new proposed rule to be added to the ruleset, the new proposed rule being based on the feature of the document that is indicative of the actual sentiment.

8. The computer-implemented method of claim 1, further comprising identifying a triggered rule from the ruleset that affects the proposed sentiment result, and causing the triggered rule to be displayed to a user.

9. The computer-implemented method of claim 1, further comprising identifying other documents, from a corpus of previously-analyzed documents, that would be affected by the proposed modification to the ruleset, and causing a notification to be displayed to a user, the notification indicating the other documents.

10. A sentiment analysis feedback system comprising:

one or more processors;

a sentiment analyzer, executing on at least one of the one or more processors, that analyzes a document using a ruleset to determine a proposed sentiment result associated with the document; and

a rule updater, executing on at least one of the one or more processors, that receives feedback about the proposed sentiment result, the feedback including an actual sentiment associated with the document and a feature of the document that is indicative of the actual sentiment, and generates a proposed modification to the ruleset based on the feedback.

11. The sentiment analysis feedback system of claim 10, wherein the rule updater causes the proposed modification to the ruleset to be displayed to a user, and updates the ruleset with the proposed modification in response to receiving a confirmation by the user.

12. The sentiment analysis feedback system of claim 10, wherein the rule updater generates the proposed modification to the ruleset by identifying a triggered rule from the ruleset that affects the proposed sentiment result, and generating a proposed update to the triggered rule when the proposed sentiment result does not match the actual sentiment, the proposed update to the triggered rule being generated based on the feature of the document that is indicative of the actual sentiment.

13. The sentiment analysis feedback system of claim 12, wherein the rule updater causes the triggered rule and the proposed update to the triggered rule to be displayed to a user.

14. The sentiment analysis feedback system of claim 10, wherein the rule updater generates the proposed modification to the ruleset by generating a new proposed rule to be added to the ruleset, the new proposed rule being based on the feature of the document that is indicative of the actual sentiment.

15. A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

generate a proposed sentiment result associated with a document, the proposed sentiment result being generated based on a ruleset applied to the document;

receive feedback about the proposed sentiment result, the feedback including an actual sentiment associated with the document and a classification associated with the document; and

identify a proposed modification to the ruleset based on the feedback.