ASPECT-BASED SENTIMENT ANALYSIS USING MACHINE LEARNING METHODS
Systems and methods for aspect-based sentiment analysis using machine learning methods. An example method comprises: performing, by a computer system, a syntactico-semantic analysis of at least part of a natural language text to produce a plurality of syntactico-semantic structures representing the part of the natural language text; interpreting the syntactico-semantic structures using a set of production rules to detect, within the part of the natural language text, at least one aspect term representing an aspect associated with a target entity; and evaluating, using one or more text characteristics produced by the syntactico-semantic analysis, a classifier function to determine a polarity associated with the aspect term.
1 . A method, comprising;
performing, by a computer system, a syntactico-semantic analysis of at least part of a natural language text to produce a plurality of syntactico-semantic structures representing the part of the natural language text;
interpreting the syntactico-semantic structures using a set of production rules to detect, within the part of the natural language text, at least one aspect term representing an aspect associated with a target entity; and
evaluating, using one or more text characteristics produced by the syntactico-semantic analysis, a classifier function to determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises a pragmatic class associated with a semantic class of a constituent representing the aspect term.
2 . The method of claim 1 , wherein the natural language text represents a plurality of consumer reviews of the target entity.
3 . The method of claim 1 , wherein the target entity is represented by at least one of: a consumer product or a service.
4 . The method of claim 1 , wherein the aspect represents at least one of: a feature, a function, or a component of the target entity.
5 . The method of claim 1 , wherein the aspect term comprises one or more words.
6 . The method of claim 1 , wherein the polarity associated with the aspect term is represented by one of: a negative polarity, a neutral polarity, or a positive polarity.
7 . The method of claim 1 , further comprising:
generating a report comprising one or more hierarchical lists of aspect terms referencing the identified aspects and polarities of the identified aspects.
8 . The method of claim 1 , wherein the classifier function is represented by one of: a linear classifier, a linear tree classifier, a random forest classifier, a conditional random field (CRF) classifier, a latent Dirichlet allocation (LDA) classifier, a support vector machine (SVM) classifiers, or a neural network-based classifier.
9 . The method of claim 1 , wherein the domain of the classifier function further comprises at least one of: a value of a grammatical attribute characterizing the aspect term, a value of a syntactic attribute characterizing the aspect term, or a value of a semantic attribute characterizing the aspect term, wherein the value is produced by the syntactico-semantic analysis.
10 . The method of claim 1 , further comprising:
determining, using a training data set, at least one parameter of the classifier function, wherein the training data set comprises a training natural language text comprising a plurality of aspect terms.
11 . The method of claim 1 , wherein each syntactico-semantic structure of the plurality of syntactico-semantic structures is represented by a graph comprising a plurality of nodes corresponding to a plurality of syntactico-semantic classes and a plurality of edges corresponding to a plurality of syntactico-semantic relationships.
12 . The method of claim 1 , wherein a production rule comprises one or more logical expressions defined on one or more syntactico-semantic structure templates.
13 . A system, comprising:
a memory; and
a processor, coupled to the memory, the processor configured to:
perform a syntactico-semantic analysis of at least part of a natural language text to produce a plurality of syntactico-semantic structures representing the part of the natural language text;
interpret the syntactico-semantic structures using a set of production rules to detect, within the part of the natural language text, at least one aspect term representing an aspect associated with a target entity; and
evaluate, using one or more text characteristics produced by the syntactico-semantic analysis, a classifier function to determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises a pragmatic class associated with a semantic class of a constituent representing the aspect term.
14 . The system of claim 13 , wherein the aspect represents at least one of: a feature, a function, or a component of the target entity.
15 . The system of claim 13 , wherein the polarity associated with the aspect term is represented by one of: a negative polarity, a neutral polarity, or a positive polarity.
16 . The system of claim 13 , wherein the processor is further configured to:
generate a report comprising one or more hierarchical lists of aspect terms referencing the identified aspects and polarities of the identified aspects.
17 . The system of claim 13 , wherein the classifier function is represented by a support vector machine (SVM) classifier.
18 . The system of claim 13 , wherein the processor is further configured to:
determine, using a training data set, at least one parameter of the classifier function, wherein the training data set comprises a training natural language text comprising a plurality of aspect terms.
19 . The system of claim 13 , wherein each syntactico-semantic structure of the plurality of syntactico-semantic structures is represented by a graph comprising a plurality of nodes corresponding to a plurality of semantic classes and a plurality of edges corresponding to a plurality of semantic relationships.
20 . A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:
perform a syntactico-semantic analysis of at least part of a natural language text to produce a plurality of syntactico-semantic structures representing the part of the natural language text;
interpret the syntactico-semantic structures using a set of production rules to detect, within the part of the natural language text, at least one aspect term representing an aspect associated with a target entity; and
evaluate, using one or more text characteristics produced by the syntactico-semantic analysis, a classifier function to determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises a pragmatic class associated with a semantic class of a constituent representing the aspect term.