SYSTEMS AND METHODS FOR DETECTING SENTIMENT-BASED TOPICS
First Claim
1. A computer implemented method for analyzing sentiment concerning a subject, comprising the steps of:
- collecting an object from an 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 an at least one sentiment category;
creating 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 a sentiment topic word candidates or a non-sentiment topic word candidate, wherein the non-sentiment topic word candidates are filtered by using a stoplist and a list of sentiment words;
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.
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Abstract
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.
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Citations
18 Claims
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1. A computer implemented method for analyzing sentiment concerning a subject, comprising the steps of:
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collecting an object from an 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 an at least one sentiment category; creating 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 a sentiment topic word candidates or a non-sentiment topic word candidate, wherein the non-sentiment topic word candidates are filtered by using a stoplist and a list of sentiment words; 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. - View Dependent Claims (2, 3, 4, 5, 6, 12)
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7. A method for analyzing sentiment concerning a subject, comprising the steps of:
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collecting an object from an 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 an at least one sentiment category; creating 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 a sentiment topic word candidates or a non-sentiment topic word candidate, wherein the non-sentiment topic word candidates are filtered by using a stoplist and a list of sentiment words; identify 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. - View Dependent Claims (8, 9, 10, 11)
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13. A computer program product comprising a 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 the steps of:
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collecting an object from an 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 an at least one sentiment category; creating 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 a sentiment topic word candidates or a non-sentiment topic word candidate, wherein the non-sentiment topic word candidates are filtered by using a stoplist and a list of sentiment words; 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. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification