Sentiment Analysis From Social Media Content

• US 20130091117A1
• Filed: 09/30/2012
• Published: 04/11/2013
• Est. Priority Date: 09/30/2011
• Status: Active Grant

• Current Assignee: Interos, Inc.
• Original Assignee: Metavana Incorporated
• Inventors: Minh, Duong-Van
• Granted Patent: US 8,849,826 B2
• Time in Patent Office: Days
• US Class Current: 707/709
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques

Abstract

The sentiment engine includes a sentiment module configured to gather opinions or determine sentiment expressed in documents, a crawling module configured to crawl servers 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 (SNPS) configured to calculate a loyalty metric of users from social media websites. The functionality of these modules may be combined with one another or in addition to other modules.

Claims

1. A computer-implemented method for sentiment analysis from social media content, comprising:

crawling, by a processor, a plurality of websites to obtain metadata from social media content;

extracting, by a processor, the metadata from the social media content by identifying a polarity of the sentiment-bearing keywords in a sentence from social media content using a phase transition formula;

determining at least one category corresponding to the at least one sentiment-bearing keyword of the sentence; and

determining at least one sentiment corresponding to the at least one category based on the at least one sentiment-bearing keyword.

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

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

4. 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.

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

6. 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.

7. 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.

8. 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.

9. The method of claim 8, wherein prior to obtaining the plurality of category specturms, 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.

10. The method of claim 1, wherein the at least one sentiment is an expression of an opinion relating to at least one category.

11. The method of claim 1, wherein the at least one category is associated with a product.

12. The method of claim 1, wherein the at least one category is associated with a service.

15. The system of claim 2, wherein the phase transition formula is

13. 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;

crawling, by a processor, a plurality of websites to obtain metadata from social media content;

extracting, by a processor, the metadata from the social media content by identifying a polarity of the sentiment-bearing keywords in a sentence from social media content using a phase transition formula;

determining at least one category corresponding to the at least one sentiment-bearing keyword of the sentence; and

determining at least one sentiment corresponding to the at least one category based on the at least one sentiment-bearing keyword.

14. The system of claim 13, wherein further comprising instructions to extract the list of keywords, wherein the instructions to extract the list of keywords from the document include instructions to:

extract keywords from each document of the plurality of documents;

for each keyword,

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

use 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

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

16. A computer readable storage medium storing at least one program configured for execution by a computer, the at least one program comprising instructions to:

crawling, by a processor, a plurality of websites to obtain metadata from social media content;

extracting, by a processor, the metadata from the social media content by identifying a polarity of the sentiment-bearing keywords in a sentence from social media content using a phase transition formula;

determining at least one category corresponding to the at least one sentiment-bearing keyword of the sentence; and

determining at least one sentiment corresponding to the at least one category based on the at least one sentiment-bearing keyword.