Applying a genetic algorithm to compositional semantics sentiment analysis to improve performance and accelerate domain adaptation
First Claim
1. A method, in a data processing system, for applying a genetic algorithm to semantic sentiment analysis, the method comprising:
- providing, by the data processing system, a sentiment analysis model to a sentiment analysis algorithm;
training, by the data processing system, the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model, wherein training the sentiment analysis model using the genetic algorithm comprises for each given training document in the training corpus;
performing the sentiment analysis algorithm on the given training document to form a sentiment analysis result;
making modifications to the sentiment analysis model to form a plurality of individual models;
identifying a best fitness individual model from the plurality of individual models using the genetic algorithm, wherein the best fitness individual model minimizes a distance from desired sentiment analysis values corresponding to the given training document, wherein fitness of each individual model is based on an absolute value of a distance from a generated sentiment value to a desired sentiment value weighted by an amount of change from an initial value in the sentiment analysis model; and
storing the best fitness individual model as the trained sentiment analysis model;
performing, by the data processing system, the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result;
outputting, by the data processing system, the domain-specific sentiment analysis result;
providing the domain-specific sentiment analysis result to a question answering system; and
performing analysis of an input question or a candidate answer in the question answering system using the domain-specific sentiment analysis result.
1 Assignment
0 Petitions
Accused Products
Abstract
A mechanism is provided in a data processing system for applying a genetic algorithm to semantic sentiment analysis. The mechanism provides a sentiment analysis model to a sentiment analysis algorithm. The mechanism trains the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model. The mechanism performs the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result. The mechanism outputs the domain-specific sentiment analysis result.
-
Citations
15 Claims
-
1. A method, in a data processing system, for applying a genetic algorithm to semantic sentiment analysis, the method comprising:
-
providing, by the data processing system, a sentiment analysis model to a sentiment analysis algorithm; training, by the data processing system, the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model, wherein training the sentiment analysis model using the genetic algorithm comprises for each given training document in the training corpus; performing the sentiment analysis algorithm on the given training document to form a sentiment analysis result; making modifications to the sentiment analysis model to form a plurality of individual models; identifying a best fitness individual model from the plurality of individual models using the genetic algorithm, wherein the best fitness individual model minimizes a distance from desired sentiment analysis values corresponding to the given training document, wherein fitness of each individual model is based on an absolute value of a distance from a generated sentiment value to a desired sentiment value weighted by an amount of change from an initial value in the sentiment analysis model; and storing the best fitness individual model as the trained sentiment analysis model; performing, by the data processing system, the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result; outputting, by the data processing system, the domain-specific sentiment analysis result; providing the domain-specific sentiment analysis result to a question answering system; and performing analysis of an input question or a candidate answer in the question answering system using the domain-specific sentiment analysis result. - View Dependent Claims (2, 3, 4, 5)
-
-
6. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:
-
provide a sentiment analysis model to a sentiment analysis algorithm; train the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model;
wherein training the sentiment analysis model comprises for each given training document in the training corpus;performing the sentiment analysis algorithm on the given training document to form a sentiment analysis result; making modifications to the sentiment analysis model to form individual models; identifying a best fitness individual model from the plurality of individual models using the genetic algorithm, wherein the best fitness individual model minimizes a distance from desired sentiment analysis values corresponding to the given training document, wherein fitness of each individual model is based on an absolute value of distance from a generated sentiment value to a desired sentiment value weighted by an amount of change from an initial value in the sentiment analysis model; and storing the best fitness individual model as the trained sentiment analysis model; perform the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result; and output the domain-specific sentiment analysis result; provide the domain-specific sentiment analysis result to a question answering system; and perform analysis of an input question or a candidate answer in the question answering system using the domain-specific sentiment analysis result. - View Dependent Claims (7, 8, 9, 10)
-
-
11. An apparatus comprising:
-
a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to; provide a sentiment analysis model to a sentiment analysis algorithm; train the sentiment analysis model using a genetic algorithm based on a training corpus of documents with corresponding desired sentiment analysis values for a given domain to form a trained sentiment analysis model;
wherein training the sentiment analysis model comprises for each given training document in the training corpus;performing the sentiment analysis algorithm on the give training document to form a sentiment analysis result; making modifications to the sentiment analysis model to form individual models; indentifying a best fitness individual model from the plurality of individual models using the genetic algorithm, wherein the best fitness individual model minimizes a distance from desired sentiment analysis values corresponding to the given training document, wherein fitness of each individual model is based on an absolute value of a distance from a generated sentiment value to a desired sentiment value weighted by an amount of change from an initial value in the sentiment analysis model; and storing the best fitness individual model as the trained sentiment analysis model; perform the sentiment analysis algorithm on an input document using the trained sentiment analysis model to form a domain-specific sentiment analysis result; output the domain-specific sentiment analysis result; provide the domain-specific sentiment analysis result to a question answering system; and perform analysis of an input question or a candidate answer in the question answering system using the domain-specific sentiment analysis result. - View Dependent Claims (12, 13, 14, 15)
-
Specification