Sentiment and Influence Analysis of Twitter Tweets

• US 20130103667A1
• Filed: 10/17/2012
• Published: 04/25/2013
• Est. Priority Date: 10/17/2011
• Status: Abandoned Application

• Current Assignee: Interos, Inc.
• Original Assignee: Metavana Incorporated
• Inventors: Minh, Duong-Van
• Application Number:
  US13/653,856
• Publication Number:
  US 20130103667A1
• Time in Patent Office: Days
• US Class Current: 707/709
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/02 using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

The present invention is directed to a system, method, and article of manufacture that employs a sentiment engine for conducting sentiment and influence analysis of various types of messages from the social media hosts or websites to extract opinions on different categories, which includes services, products or hotels, and others, collectively referred to as “the keyword product”. The sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to servers of social network websites to obtain at least a subset of the documents or opinions from social media websites, a keyword module configured to extract keywords from documents, a filtering module configured to filter keywords and documents, and a classification module configured to classify documents, sentences, and/or keywords, a polarity prediction module configured to predict the polarity of a sentiment sentence, and a social media net promoter score configured to calculate a loyalty metric of users from social media websites, and a message analysis module configured to conduct analysis of a message from host social media sites, forums, blogs and product/service providers. The message analysis module includes analyzing message from other host social media sites.

Claims

1. A computer-implemented method for sentiment and influential analysis, comprising:

receiving, by a processor, a plurality electronic messages posted by one or more users on social media web websites;

identifying, by a processor, a polarity of the sentiment-bearing keywords for each electronic message using a phase transition formula;

determining, by a processor, at least one category corresponding to the at least one sentiment-bearing keyword associated with each electronic message; and

determining, by a processor, an influence attribute for each electronic message based on a plurality of influence factors.

2. The method of claim 1, prior to the receiving step, further comprising crawling, by processor, a plurality of social media websites to obtain electronic messages.

3. The method of claim 1, prior to the receiving step, further comprising crawling, by a processor, a plurality of websites to obtain metadata from social media websites.

4. The method of claim 1, after the determining at least one category step, determining at least one sentiment corresponding to the at least one category based on the at least one sentiment-bearing keyword.

5. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise determining the number of people following an author associated with each message.

6. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise the number of electronic messages that an author has created

7. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise the number of resending or forwarding a particular electronic message

8. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise the number of people replying to a particular electronic message.

9. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise extracting information from the social media websites the number of people that expressed liking a particular electronic message.

10. The method of claim 1, wherein the plurality of influence factors in the influence attribute comprise determining the number of people an author of a particular electronic message is following.

11. The method of claim 1, wherein the electronic messages comprises message feeds from the social media. websites.

12. The method of claim 1, wherein the extracting step comprises filtering the sentiment-bearing keywords with sentiment eliminators.

13. The method claim 1, wherein the extracting step comprises filtering the sentiment-bearing keywords by associating with a sentiment score.

14. The method of claim 1, wherein extracting step comprises extracting opinion bearing keywords from social media content;

for each keyword,

calculating a frequency, f, of the keyword in the plurality of documents and a number of documents, N, that include the keyword;

using the phase transition formula to calculate the relevancy of the keyword based on the frequency of the keyword in the plurality of documents and the number of documents that include the keyword; and

adding the keyword to the list of keywords when the relevancy of the keyword exceeds a predetermined threshold.

15. The computer-implemented method of claim 4, wherein the phase transition formula is

16. The method of claim 1, wherein prior to determining step, the method further comprises generating the list of categories by:

determining pairs of keywords in the list of keywords that are related to each other, wherein the pairs of keywords are unique pairs of keywords;

identifying sets of the pairs of the keywords in which each set includes at least one keyword that is common to all of the pairs of keywords in the set; and

until a predetermined termination condition is achieved, iteratively combining the set of the pairs of keywords in which each combined set includes at least one keyword that is common to all of the pairs of keywords in the combined set.

17. The method of claim 1, wherein determining the at least one category corresponding to the at least one keyword of the sentence includes using a neural network to determine the at least one category corresponding to the at least one keyword of the sentence.

18. The method of claim 1, wherein determining the at least one category corresponding to the at least one keyword of the sentence includes:

obtaining a plurality of category spectrums, a respective category spectrum including a frequency of occurrence of keywords in the list of keywords that corresponds to a respective category,

determining a category spectrum for the sentence based on the at least one keyword;

calculating dot products of the category spectrum for the sentence and each category spectrum in the plurality of category spectrums; and

determining the at least one category as a category corresponding to at least one dot product that exceeds a predetermined threshold.

19. The method of claim 18, wherein prior to obtaining the plurality of category spectrums, the method further comprises for each category, determining a category spectrum for the category by:

obtaining a corpus of documents corresponding to the category;

extracting keywords from each document in the corpus of documents;

filtering the keywords using the phase transition formula to produce filtered keywords;

determining the frequency of occurrence of the filtered keywords in the corpus of documents; and

normalizing the frequency of occurrence of the filtered keywords to produce the category spectrum for the category.

20. A system to determine sentiment expressed in a document, comprising:

at least one processor;

memory; and

at least one program stored in the memory, the at least one program comprising instructions to;

receiving, by a processor, a plurality electronic messages posted by one or more users on social media web websites;

identifying, by a processor, a polarity of the sentiment-bearing keywords for each electronic message using a phase transition formula;

determining, by a processor, at least one category corresponding to the at least one sentiment-bearing keyword associated with each electronic message; and

determining, by a processor, an influence attribute for each electronic message based on a plurality of influence factors.