Visualization of reputation ratings

• US 10,127,583 B2
• Filed: 01/30/2015
• Issued: 11/13/2018
• Est. Priority Date: 04/16/2007
• Status: Expired due to Fees

• Current Assignee: eBay Inc.
• Original Assignee: eBay Inc.
• Inventors: Sundaresan, Neelakantan, Ganesan, Kavita, Deo, Harshal Ulhas
• Primary Examiner(s): Channavajjala, Srirama
• Application Number:
  US14/611,087
• Publication Number:
  US 20150149385A1
• Time in Patent Office: 1,383 Days
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/168 Details of user interfaces specifically adapted to file systems, e.g. browsing and visualisation, 2d or 3d GUIs query results presentation G06F16/156
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/3323 using document space presentation or visualization, e.g. category, hierarchy or range presentation and selection
• CPC Class Codes: G06F 16/3326 using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages
• CPC Class Codes: G06F 16/338 Presentation of query results
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06F 16/958 Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
• CPC Class Codes: G06F 3/14 Digital output to display device ; Cooperation and interconnection of the display device with other functional units
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

In one embodiment, a system and method is illustrated including receiving a feedback request identifying a particular user, retrieving a feedback entry in response to the feedback request, the feedback entry containing a first term, building a scoring model based, in part, upon a term frequency count denoting a frequency with which the first term appears in a searchable data structure, mapping the first term to a graphical illustration based upon a second term associated with the graphical illustration such that the graphical illustration may be used to represent the second term, and generating a feedback page containing the first term and the graphical illustration. The method may include assigning a value to the first term so as to identify the first term, assigning the first term to the searchable data structure, and extracting the first term from the searchable data structure based, in part, upon an extraction rule.

Claims

1. A system comprising:

one or more hardware processors, configured to;

receive, from a network, a feedback request requested via a user interface and for a feedback page, the feedback request identifying a particular product;

extract phrases from a plurality of textual feedback entries that are about the particular product identified in the feedback request, the extracted phrases comprising a first phrase and a second phrase;

determine a score for each of the extracted phrases based, at least in part, on a frequency of the extracted phrases in the textual feedback entries;

determine a first gradation for a first visual indication in the feedback page that corresponds to the first phrase based on the score for the first phrase;

determine a second gradation for a second visual indication in the feedback page that corresponds to the second phrase based on the score for the second phrase;

generate the feedback page as a response to the feedback request, the feedback page including text data corresponding to at least the first and second phrases and generated, in part, by applying the first gradation to the first visual indication corresponding to the first phrase at a first position in the feedback request and applying the second gradation to the second visual indication corresponding to the second phrase at a second position in the feedback page; and

transmit the feedback page over the network to a client device associated with the feedback request.

2. The system of claim 1, wherein the one or more hardware processors are further configured to filter noise words from the plurality of textual feedback entries based on a dictionary of the noise words.

3. The system of claim 1, wherein the one or more hardware processors are further configured to determine the first and second gradations from different highlights, including at least one of a font size highlight, a color highlight, an underline highlight, or a bold highlight.

4. The system of claim 1, wherein at least one of the first or second visual indications is an emoticon.

5. The system of claim 1, wherein the first visual indication is a textual representation of the first phrase and the second visual indication is a textual representation of the second phrase.

6. The system of claim 5, wherein the one or more hardware processors are further configured to:

categorize the extracted phrases; and

generate the feedback page to include an emoticon adjacent to the textual representation of the first phrase based on the categorization of the first phrase and a different emoticon adjacent to the textual representation of the second phrase based on the categorization of the second phrase.

7. The system of claim 1, wherein the one or more hardware processors are further configured to determine a count for each of the extracted phrases in the plurality of textual feedback entries, wherein the score for each of the extracted phrases is determined based, in part, on a respective count of the extracted phrase and a number of the textual feedback entries.

8. The system of claim 7, wherein the respective count of the extracted phrase is based on an aggregation of a count of each word within the extracted phrase.

9. The system of claim 7, wherein the one or more hardware processors are further configured to determine the first gradation for the first visual indication based on a product of the respective count of the first phrase and an inverse document frequency value.

10. The system of claim 7, wherein the one or more hardware processors are further configured to determine the score for the first phrase further based on determining a relation between the respective count of the first phrase and the number of textual feedback entries, and the first gradation for the first visual indication is determined further based on the relation.

11. The system of claim 1, wherein the one or more hardware processors are further configured to retrieve the plurality of textual feedback entries for extracting the phrases based on an identifier included in the feedback request identifying the particular product.

12. A method comprising:

receiving, from a network, a feedback request requested via a user interface and for a feedback page, the feedback request identifying a particular product;

extracting phrases from a plurality of textual feedback entries that are about the particular product identified in the feedback request, the extracted phrases comprising a first phrase and a second phrase;

determining a score for each of the extracted phrases based, at least in part, on a frequency of the extracted phrases in the textual feedback entries;

determining a first gradation for a first visual indication in the feedback page that corresponds to the first phrase based on the score for the first phrase;

determining a second gradation for a second visual indication in the feedback page that corresponds to the second phrase based on the score for the second phrase;

generating the feedback page as a response to the feedback request, the feedback page including text data corresponding to at least the first and second phrases and

generated, in part, by applying the first gradation to the first visual indication corresponding to the first phrase at a first position in the feedback request and applying the second gradation to the second visual indication corresponding to the second phrase at a second position in the feedback page; and

transmitting the feedback page over the network to a client device associated with the feedback request.

13. The method of claim 12, wherein the feedback request includes an identifier identifying the particular product.

14. The method of claim 12, further comprising:

filtering noise words from the plurality of textual feedback entries based on a dictionary of the noise words; and

mapping the first phrase based on a searchable data structure to the first visual indication, the searchable data structure including at least one of positive, neutral, or negative feedback entries.

15. The method of claim 12, further comprising determining a count for each of the extracted phrases in the plurality of textual feedback entries, wherein the score for each of the extracted phrases is determined based, in part, on a respective count of the extracted phrase and a number of the textual feedback entries.

16. The method of claim 15, wherein the respective count of the extracted phrase is based on an aggregation of a count of each word within the extracted phrase.

17. The method of claim 15, further comprising determining the first gradation for the first visual indication based on a product of the respective count of the first phrase and an inverse document frequency value.

18. The method of claim 17, wherein the first and second gradations are determined from different highlights, including at least one of a font size highlight, a color highlight, an underline highlight, or a bold highlight.

19. The method of claim 12, further comprising:

categorizing the extracted phrases; and

generating the feedback page to include an emoticon adjacent to the first phrase based on the categorization of the first phrase and a different emoticon adjacent to the second phrase based on the categorization of the second phrase.

20. A non-transitory machine-readable storage medium comprising instructions, which when executed by one or more processors of a machine, cause the machine to perform operations comprising:

receiving, from a network, a feedback request requested via a user interface and for a feedback page, the feedback request identifying a particular product;

extracting phrases from a plurality of textual feedback entries that are about the particular product identified in the feedback request, the extracted phrases comprising a first phrase and a second phrase;

determining a score for each of the extracted phrases based, at least in part, on a frequency of the extracted phrases in the textual feedback entries;

determining a first gradation for a first visual indication in the feedback page that corresponds to the first phrase based on the score for the first phrase;

determining a second gradation for a second visual indication in the feedback page that corresponds to the second phrase based on the score for the second phrase;

generating the feedback page as a response to the feedback request, the feedback page including text data corresponding to at least the first and second phrases and

generated, in part, by applying the first gradation to the first visual indication corresponding to the first phrase at a first position in the feedback request and applying the second gradation to the second visual indication corresponding to the second phrase at a second position in the feedback page; and

transmitting the feedback page over the network to a client device associated with the feedback request.

21. The machine-readable storage medium of claim 20, wherein the operations further comprise:

categorizing the extracted phrases; and

generating the feedback page to include an emoticon adjacent to the first phrase based on the categorization of the first phrase and a different emoticon adjacent to the second phrase based on the categorization of the second phrase.

22. The machine-readable storage medium of claim 20, wherein operations further comprise retrieving the plurality of textual feedback entries for extracting the phrases based on an identifier included in the feedback request identifying the particular product.

23. The machine-readable storage medium of claim 20, wherein operations further comprise:

filtering noise words from the plurality of textual feedback entries based on a dictionary of the noise words; and

mapping the first phrase based on a searchable data structure to the first visual indication, the searchable data structure including at least one of positive, neutral, or negative feedback entries.