Systems and methods for detecting sentiment-based topics
A method for analyzing sentiment comprising: collecting an object from an external content repository, the collected objects forming a content database; extracting a snippet related to the subject from the content database; calculating a sentiment score for the snippet; classifying the snippet into a sentiment category; creating sentiment taxonomy using the sentiment categories, the sentiment taxonomy classifying the snippets as positive, negative or neutral; identifying topic words within the sentiment taxonomy; classifying the topic words as a sentiment topic word candidates or a non-sentiment topic word candidate, filtering the non-sentiment topic word candidates; identifying the frequency of the non-sentiment topic words in each of the sentiment categories; identifying the importance of the non-sentiment topic word for each of the sentiment categories; and, ranking the topic word, wherein the rank is calculated by combining the frequency of the topic words in each of the categories with its importance.
1. A computer implemented method for analyzing sentiment concerning a subject, comprising:
collecting a plurality of objects from at least one external content repository, wherein the collected objects form a content database;
extracting snippets related to the subject from the content database;
calculating a sentiment score for at least one snippet that has been extracted;
for the at least one snippet for which the sentiment score has been calculated, classifying the snippet into at least one sentiment category;
creating a sentiment taxonomy using the sentiment categories, wherein the sentiment taxonomy classifies the snippets as positive, negative or neutral;
identifying topic words within the sentiment taxonomy;
classifying the topic words as sentiment topic words or non-sentiment topic words;
identifying the frequency of the non-sentiment topic words in each of the sentiment categories;
identifying the importance of the non-sentiment topic words in each of the sentiment categories; and,
ranking the non-sentiment topic words, wherein the rank for a non-sentiment topic word is calculated by combining the frequency of the non-sentiment topic word in each of the sentiment categories with the importance of the non-sentiment topic word.
2. The computer implemented method as in claim 1 wherein one external content repository is one from a group consisting of:
a blog;
a message board;
a web page article; and
a comment feed.
3. The computer implemented method as in claim 1 wherein identifying the importance of the non-sentiment topic words in each of the sentiment categories comprises calculating for each non-sentiment topic word a Pointwise Mutual Information of the non-sentiment topic word.
4. The computer implemented method as in claim 1 wherein identifying the importance of the non-sentiment topic words in each of the sentiment categories comprises calculating for each non-sentiment topic word a word support value for the non-sentiment topic word.
5. The computer implemented method as in claim 4 wherein identifying the importance of the non-sentiment topic word in each of the sentiment categories further comprises calculating for each non-sentiment topic word a Pointwise Mutual Information value for the non-sentiment topic word.
6. A system for analyzing sentiment concerning a subject, comprising a processor and a storage coupled to the processor, the processor configured to:
collect a plurality of objects from at least one external content repository, wherein the collected objects form a content database;
extract snippets related to the subject from the content database;
calculate a sentiment score for at least one snippet that has been extracted;
for the at least one snippet for which the sentiment score has been calculated, classify the snippet into at least one sentiment category;
create a sentiment taxonomy using the sentiment categories, wherein the sentiment taxonomy classifies the snippets as positive, negative or neutral;
identify topic words within the sentiment taxonomy;
classify the topic words as sentiment topic words or non-sentiment topic words;
identify the frequency of the non-sentiment topic words in each of the sentiment categories;
identify the importance of the non-sentiment topic words in each of the sentiment categories; and,
rank the non-sentiment topic words, wherein the rank for a non-sentiment topic word is calculated by combining the frequency of the non-sentiment topic word in each of the sentiment categories with the importance of the non-sentiment topic word.
7. The method system as in claim 6 wherein one external content repository is one from a group consisting of:
a blog;
a message board;
a web page article; and
a comment feed.
8. The system as in claim 6 wherein the processor is further configured, in identifying the importance of the non-sentiment topic words in each of the sentiment categories, to calculate for each non-sentiment topic word a Pointwise Mutual Information of the non-sentiment topic word.
9. The system as in claim 6 wherein the processor is further configured, in identifying the importance of the non-sentiment topic words in each of the sentiment categories, to calculate for each non-sentiment topic word a word support value for the non-sentiment topic word.
10. The system as in claim 6 wherein the processor is further configured, in identifying the importance of the non-sentiment topic word in each of the sentiment categories, to calculate for each non-sentiment topic word a Pointwise Mutual Information value for the non-sentiment topic word.
11. A computer program product comprising a non-transitory computer useable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform for operations for determining analyzing sentiment concerning a subject, comprising:
collecting a plurality of objects from at least one external content repository, wherein the collected objects form a content database;
extracting snippets related to the subject from the content database;
calculating a sentiment score for at least one snippet that has been extracted;
for the at least one snippet for which the sentiment score has been calculated, classifying the snippet into at least one sentiment category;
creating a sentiment taxonomy using the sentiment categories, wherein the sentiment taxonomy classifies the snippets as positive, negative or neutral;
identifying topic words within the sentiment taxonomy;
classifying the topic words as sentiment topic words or non-sentiment topic words;
identifying the frequency of the non-sentiment topic words in each of the sentiment categories;
identifying the importance of the non-sentiment topic words in each of the sentiment categories; and,
ranking the non-sentiment topic words, wherein the rank for a non-sentiment topic word is calculated by combining the frequency of the non-sentiment topic word in each of the sentiment categories with the importance of the non-sentiment topic word.
12. The computer program product as in claim 11 wherein one external content repository is one from a group consisting of:
a blog;
a message board;
a web page article; and
a comment feed.
13. The computer program product as in claim 11 wherein identifying the importance of the non-sentiment topic words in each of the sentiment categories comprises calculating for each non-sentiment topic word a Pointwise Mutual Information of the non-sentiment topic word.
14. The computer program product as in claim 11 wherein identifying the importance of the non-sentiment topic words in each of the sentiment categories comprises calculating for each non-sentiment topic word a word support value for the non-sentiment topic word.
15. The computer program product as in claim 11 wherein identifying the importance of the non-sentiment topic word in each of the sentiment categories further comprises calculating for each non-sentiment topic word a Pointwise Mutual Information value for the non-sentiment topic word.