Method for updating a knowledge base of a sentiment analysis system
First Claim
1. A method, in a data processing apparatus comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor to cause the at least one processor to be configured to implement a sentiment analysis system for updating a knowledge base of the sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term, the method comprising:
- receiving messages comprising natural language from one or more public social media systems;
deciding using content of the knowledge base, whether at least one message of the received messages has a positive sentiment or a negative sentiment;
classifying the received messages into a positive set of messages having a positive sentiment and a negative set of messages having a negative sentiment;
extracting a term from the at least one message that is not present in the knowledge base;
based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, calculating a score value of the term; and
storing the term and the calculated score value into the knowledge base, wherein the term is extracted from the message of one of the positive set of messages or the negative set of messages, and wherein the frequency of occurrence is the fraction of messages of the one of the positive set of messages and negative set of messages that contain the term.
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Abstract
A mechanism is provided updating a knowledge base of a sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term. Messages comprising natural language are received. Using content of the knowledge base, a decision is made as to whether at least one message of the received messages has a positive sentiment or a negative sentiment. A term is extracted from the message that is not present in the knowledge base. Based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, a score value of the term is calculated, and the term and the calculated score value are stored into the knowledge base.
9 Citations
17 Claims
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1. A method, in a data processing apparatus comprising at least one processor and at least one memory, the at least one memory comprising instructions that are executed by the at least one processor to cause the at least one processor to be configured to implement a sentiment analysis system for updating a knowledge base of the sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term, the method comprising:
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receiving messages comprising natural language from one or more public social media systems; deciding using content of the knowledge base, whether at least one message of the received messages has a positive sentiment or a negative sentiment; classifying the received messages into a positive set of messages having a positive sentiment and a negative set of messages having a negative sentiment; extracting a term from the at least one message that is not present in the knowledge base; based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, calculating a score value of the term; and storing the term and the calculated score value into the knowledge base, wherein the term is extracted from the message of one of the positive set of messages or the negative set of messages, and wherein the frequency of occurrence is the fraction of messages of the one of the positive set of messages and negative set of messages that contain the term. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable storage medium is not a transitory signal per se, and wherein the computer readable program, when executed on a computing device, causes the computing device to implement a sentiment analysis system for updating a knowledge base of the sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term, and further causes the computing device to:
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receive messages comprising natural language from one or more public social media systems; decide using content of a knowledge base, whether at least one message of the received messages has a positive sentiment or a negative sentiment; classify the received messages into a positive set of messages having a positive sentiment and a negative set of messages having a negative sentiment; extract a term from the at least one message that is not present in the knowledge base; based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, calculate a score value of the term; and store the term and the calculated score value into the knowledge base, wherein the term is extracted from the message of one of the positive set of messages or the negative set of messages, and wherein the frequency of occurrence is the fraction of messages of the one of the positive set of messages and negative set of messages that contain the term. - View Dependent Claims (13, 14)
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15. An apparatus comprising:
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a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a sentiment analysis system for updating a knowledge base of the sentiment analysis system, the knowledge base being operable for storing natural language terms and a score value related to each natural language term, the score value characterizing the sentiment of the natural language term, and further causes the processor to; receive multiple messages comprising natural language from one or more public social media systems; decide using the content of a knowledge base, whether at least one message of the received messages has a positive or a negative sentiment; classify the received messages into a positive set of messages having a positive sentiment and a negative set of messages having a negative sentiment; extract a term from the at least one message that is not present in the knowledge base; based on a frequency of occurrence of the term in the received messages and the sentiment of the messages in which the term occurs, calculate a score value of the term; and store the term and the calculated score value into the knowledge base, wherein the term is extracted from the message of one of the positive set of messages or the negative set of messages, and wherein the frequency of occurrence is the fraction of messages of the one of the positive set of messages and negative set of messages that contain the term. - View Dependent Claims (16, 17)
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Specification