Phrase Based Snippet Generation

• US 20090193011A1
• Filed: 01/25/2008
• Published: 07/30/2009
• Est. Priority Date: 01/25/2008
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Reynar, Jeffrey C., McDonald, Ryan, Blair-Goldensohn, Sasha, Hannan, Kerry, Neylon, Tyler
• Granted Patent: US 8,010,539 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/345 Summarisation for human users
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

Disclosed herein is a method, a system and a computer product for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity. One or more textual reviews associated with the entity is selected. A plurality of sentiment phrases are identified based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity. One or more sentiment phrases from the plurality of sentiment phrases are selected to generate a snippet.

Claims

1. A computer-implemented method for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity, the method comprising:

selecting one or more textual reviews associated with the entity;

identifying a plurality of sentiment phrases based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity;

selecting one or more sentiment phrases from the plurality of sentiment phrases;

generating a snippet based on the selected one or more sentiment phrases; and

storing the snippet.

2. The method of claim 1, further comprising displaying the snippet in association with a search result.

3. The method of claim 2, wherein the search results is displayed responsive to a search query comprising an entity type associated with the entity.

4. The method of claim 1, wherein identifying a plurality of sentiment phrases comprises identifying a noun phrase corresponding to a property of the entity and a adjective associated with the noun phrase.

5. The method of claim 4, wherein identifying a noun phrase corresponding to a property of the entity and an adjective associated with the noun phrase comprises identifying a match between a regular expression and a textual review.

6. The method of claim 4, further comprising:

generating a sentiment score for each of the plurality of sentiment phrases, wherein each sentiment score is based at least in part on the adjective associated with the noun phrase.

7. The method of claim 1, further comprising:

generating a sentiment score for each of the plurality of sentiment phrases; and

selecting one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the sentiment scores.

8. The method of claim 1, further comprising:

generating plurality of frequency values for the plurality of sentiment phrases, wherein each frequency value represents a number of times a sentiment phrase is identified in the one or more of textual reviews; and

selecting one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the frequency values.

9. The method of claim 8, wherein selecting one or more sentiment phrases comprises:

determining at least a first group of sentiment scores based on the noun phrase; and

selecting a representative sentiment phrase based on the plurality of frequency values.

10. The method of claim 1, wherein the sentiment phrases are comprised of a plurality of tokens and selecting one or more sentiment phrases comprises:

generating a redundancy metric, wherein each redundancy metric is based on a number of tokens shared between two sentiment phrases; and

selecting one or more sentiment phrases based on the redundancy metric.

11. A computer readable storage medium comprising computer program code for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity, the computer program code comprising program code for:

selecting one or more of textual reviews associated with the entity;

identifying a plurality of sentiment phrases based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity;

selecting one or more sentiment phrases from the plurality of sentiment phrases;

generating a snippet based on the selected one or more sentiment phrases; and

storing the snippet.

12. The medium of claim 11, further comprising displaying the snippet in association with a search result.

13. The medium of claim 12, wherein the search results is displayed responsive to a search query comprising an entity type associated with the entity.

14. The medium of claim 11, wherein identifying a plurality of sentiment phrases comprises identifying a noun phrase corresponding to a property of the entity and a adjective associated with the noun phrase.

15. The medium of claim 14, wherein identifying a noun phrase corresponding to a property of the entity and an adjective associated with the noun phrase comprises identifying a match between a regular expression and a textual review.

16. The medium of claim 15, further comprising:

generating a sentiment score for each of the plurality of sentiment phrases, wherein each sentiment score is based at least in part on the adjective associated with the noun phrase.

17. The medium of claim 11, further comprising:

generating a sentiment scores for each of the plurality of sentiment phrases; and

selecting one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the sentiment scores.

18. The medium of claim 17, further comprising:

generating plurality of frequency values for the plurality of sentiment phrases, wherein each frequency value represents a number of times a sentiment phrase is identified in the one or more textual reviews; and

selecting one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the frequency values.

19. The medium of claim 17, wherein selecting one or more sentiment phrases comprises:

determining at least a first group of sentiment scores based on the noun phrase; and

selecting a representative sentiment phrase based on the plurality of frequency values.

20. The medium of claim 11, wherein the sentiment phrases are comprised of a plurality of tokens and selecting one or more sentiment phrases comprises:

generating a redundancy metric, wherein each redundancy metric is based on a number of tokens shared between two sentiment phrases; and

selecting one or more sentiment phrases based on the redundancy metric.

21. A computer system for generating a snippet for an entity, wherein each snippet comprises a plurality of sentiments about the entity, the system comprising:

a database comprising a plurality of textual reviews associated with the entity;

a phrase extraction module adapted to identify a plurality of sentiment phrases based on the one or more textual reviews, wherein each sentiment phrase comprises a sentiment about the entity; and

a snippet generation module adapted to;

select one or more sentiment phrases from the plurality of sentiment phrases;

generate a snippet based on the selected one or more sentiment phrases; and

store the snippet.

22. The system of claim 21, wherein the phrase extraction module is further adapted to identify a noun phrase corresponding to a property of the entity and a adjective associated with the noun phrase.

23. The system of claim 22, further comprising a sentiment score module adapted to:

generate a sentiment scores for each of the plurality of sentiment phrases, wherein each sentiment score is based at least in part on the adjective associated with the noun phrase.

24. The system of claim 21, further comprising a sentiment score module adapted to generate a sentiment scores for each of the plurality of sentiment phrases and wherein the phrase extraction module is adapted to select one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the sentiment scores.

25. The system of claim 24, wherein:

the phrase extraction module is further adapted to generate a plurality of frequency values for the plurality of sentiment phrases, wherein each frequency value represents the number of times a sentiment phrase is identified in the one or more textual reviews; and

the phrase selection module is further adapted to select one or more sentiment phrases from the plurality of sentiment phrases based at least in part on the frequency values.