METHOD AND SYSTEM FOR CONDUCTING AN OPINION SEARCH ENGINE AND A DISPLAY THEREOF

• US 20160162582A1
• Filed: 06/12/2015
• Published: 06/09/2016
• Est. Priority Date: 12/09/2014
• Status: Abandoned Application

• Current Assignee: Interos, Inc.
• Original Assignee: Moodwire, Inc.
• Inventors: Chatterjee, Manjirnath, WATSON, Erick
• Application Number:
  US14/738,042
• Publication Number:
  US 20160162582A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24575 using context
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation

Abstract

Embodiments of the present invention provide a system, Embodiments of the present invention are directed to methods, computer program products, computer systems for providing a computing search platform for conducting opinion searches over the Internet concerning aggregated social media electronic messages about public opinions and public sentiments for wide variety of matrices, such as social media posting of a particular industry over a specified time period, electronic social media posting on the public sentiments, public buzz, public mood on US senators, or electronic social media textual data of the upcoming US presidential election of Republic and Democrat candidates. An opinion search engine serves as the backbone in complex data crunching of thousands or millions of electronic social media messages which detect, extract, compute, and correlate both unstructured textual data and structured textual data. In response to a search query submitted through an opinion search bar, the opinion search engine processes the query to return an aggregated result in a transformed visual representation of the selected one or more entities, as well as public buzz, public mood, and other public sentiments on one or more related products, to the user'"'"'s computer display.

Claims

1. A computer-implemented method for conducting an opinion search, comprising:

extracting by a computer entity information and attributes from each structured electronic social media message in the plurality of structured electronic social media messages and extracting entity information and attributes from each normalized unstructured electronic social media message in the plurality of unstructured electronic social media messages;

scoring by a computer a composite sentiment value and attributes for the text in each structured electronic social media message or each normalized unstructured electronic social media message,

storing by a computer the scored structured electronic social media messages and the scored normalized unstructured electronic social media message in a database; and

aggregating by a computer the results of the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for one or more entities organized for display as a transformed visual representation.

2. The method of claim 1, after the aggregating step, further comprising generating the transformed visual representation onto a user'"'"'s computer display in response to the search query entered by the user.

3. The method of claim 2, after the aggregating step, further comprising computing an application programming interface (API) output suitable for sending the transformed visual representation to the user'"'"'s computer display.

4. The method of claim 1, wherein the aggregating step comprising computing the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for a particular industry, the transformed visual representation organized by entities with the highest number of total structured and unstructured electronic social media messages and the corresponding opinion bias for the group of structured and unstructured electronic social media messages for a particular entity that is color coded.

5. The method of claim 1, wherein the aggregating step comprising computing the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for a particular industry, the transformed visual representation organized by entities and attributes with the highest number of total structured and unstructured electronic social media messages and the corresponding opinion bias for the group of structured and unstructured electronic social media messages for a particular entity that is color coded, the transformed visual representation including a public buzz curve over a specified period of time with the opinion amplitudes and quantities.

6. The method of claim 1, wherein the aggregating step comprising computing the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for a particular industry, the transformed visual representation organized by entities and attributes with the highest number of total structured and unstructured electronic social media messages and the corresponding opinion bias for the group of structured and unstructured electronic social media messages for a particular entity that is color coded, the transformed visual representation including a public buzz curve over a specified period of time with the opinion amplitudes and quantities, and the transformed visual representation including a public mood curve over a specified period of time with the mood amplitudes and quantities.

7. The method of claim 3, wherein the computer display displays the transformed visual representation as a geometric shape that encompasses a plurality of sub-geometric shapes, each sub-geometric shape having a geometric size that corresponds to the proportional percentage of social media electronic messages relative to the total amount for the entire geometric shape, each sub-geometric shape having a color that reflect the opinion bias for that population of the social media electronic messages.

8. The method of claim 3, wherein the computer display displaying the transformed visual representation as a tree map.

9. The method of claim 3, wherein the computer display displays the transformed visual representation as a word cloud, the word cloud having a plurality of products associated with a particular entity.

10. The method of claim 1, prior to the extracting step, further comprising receiving a large amount of social media electronic messages from one or more sources through one or more electronic communication mediums, the large amount of social media electronic messages including a plurality of unstructured electronic social media messages and a plurality of structured electronic social media messages textual data.

11. The method of claim 1, wherein the plurality of structured electronic social media message comprise a standard data format, and the plurality of normalized unstructured electronic social media messages comprise the same standard data format.

12. The method of claim 1, prior to the extracting step, further comprising detecting one or more entities from the plurality of unstructured electronic social media messages;

and normalizing the plurality of unstructured electronic social media messages having a random format to the plurality of normalized unstructured electronic social media messages having a standard format.

13. The method of claim 12, wherein the detecting step comprises receiving and detecting text, tweets, news, reviews, and other sources from various social media websites that are determined to be raw and unstructured data.

14. The method of claim 13, wherein the raw data comprises unidentified entities and opinions toward the unidentified entities.

15. The method of claim 1, wherein the one or more entities having one or more entity relationships that are established by direct opinions as extracted from one or more entities embodied in one or more structured or unstructured electronic social media messages.

16. The method of claim 1, wherein the one or more entities having one or more entity relationships that are established by aggregated opinions as extracted from one or more entities embodied in one or more structured or unstructured electronic social media messages.

17. A computer-implemented method for conducting an opinion search, comprising:

receiving a query from a user;

extracting one or more entities from the query;

matching the one or more entities with the populated entities in a database, the database including a list of entities, each entity associating with one or more attributes which provides a contextual meaning to the entity; and

returning a resulting public opinion for the entity as derived and synthesized from unstructured public textual data sources over the Internet into a computer display.

18. The method of claim 17, wherein the one or more attributes infers an ontological relationship.

19. The method of claim 18, wherein the database including a list of core entities.

20. The method of claim 17, wherein the opinion search engine comprises the list of entities with taxonomy relationship, ontology relationship, and time series data.

21. The method of claim 17, wherein the opinion search engine returns the list of entities with taxonomy relationship, ontology relationship, and time series data that contain a buzz measurement, the number of times that the entity was mentioned in a predefined period.

22. The method of claim 17, wherein the opinion search engine returns the list of entities with taxonomy relationship, ontology relationship, and time series data that contain a mood measurement, the mood measurement including one or more positive sentiments and/or one or more negative sentiments that are defined over a time period.

23. The method of claim 22, further comprising displaying the resulting public opinion in a visual graph representation.

24. The method claim 22, wherein the visual graph representation comprises a line graph of time series data that denotes the buzz measurement over a predefined period.

25. The method claim 22, wherein the visual graph representation comprises positive and negative sentiments, and a sentiment magnitude for each entity.

26. The method of claim 17, wherein the returning step comprises retuning the resulting public opinion for the entity depending on the entity type.

27. The method of claim 17, wherein one or more entities comprise two entities for comparison between the two entities.

28. The method of claim 3, wherein the transformed visual graph representation comprises comparable results with a first visual representation for a first entity and a second visual representation for a second entity.