Aspect-based sentiment analysis and report generation using machine learning methods
Systems and methods for aspect-based sentiment analysis using machine learning methods. An example method comprises: receiving, by a computer system, a custom dictionary comprising a list of lexemes referencing at least one of: a target entity or an aspect associated with the target entity; performing, using the custom dictionary, 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; evaluating, using one or more text characteristics produced by the syntactico-semantic analysis, a classifier function to determine polarities associated with one or more aspect terms; and generating a report comprising the aspect terms and polarities of aspects referenced by the aspect terms.
1. A method, comprising;
receiving, by a computer system, a custom dictionary comprising a list of lexemes referencing at least one of: a target entity or an aspect associated with the target entity;
performing, using the custom dictionary, 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 plurality of syntactico-semantic structures to detect, within the part of the natural language text, an aspect term representing an aspect associated with a target entity;
identifying, in the plurality of syntactico-semantic structures, a highest constituent having a kernel comprised by the aspect term;
evaluating a classifier function to determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises one or more attributes of a context of the highest constituent; and
generating a report comprising the aspect term and the polarity of 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 polarity associated with the aspect term is represented by one of: a negative polarity, a neutral polarity, or a positive polarity.
5. The method of claim 1 , wherein interpreting the syntactico-semantic structures is performed by applying a set of production rules.
6. The method of claim 5 , further comprising:
receiving a user input modifying the identified aspects term.
7. The method of claim 1 , further comprising:
receiving a user input modifying the identified polarity of the aspects term.
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 , 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.
10. 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.
11. The method of claim 1 , further comprising:
producing the custom dictionary represented by a semantic hierarchy comprising definitions of a plurality of semantic classes.
12. The method of claim 1 , wherein the aspect term represents one of: a function of the target entity or a component of the target entity.
13. A system, comprising:
a memory; and
a processor, coupled to the memory, the processor configured to:
receive a custom dictionary comprising a list of lexemes referencing at least one of: a target entity or an aspect associated with the target entity;
perform, using the custom dictionary, 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 plurality of syntactico-semantic structures to detect, within the part of the natural language text, an aspect term representing an aspect associated with a target entity;
identifying, in the plurality of syntactico-semantic structures, a highest constituent having a kernel comprised by the aspect term;
evaluate a classifier function to determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises one or more attributes of a context of the highest constituent; and
generate a report comprising the aspect term and the polarity of the aspect term.
14. 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.
15. The system of claim 13 , wherein interpreting the syntactico-semantic structures is performed by applying a set of production rules.
16. The system of claim 15 , wherein the processor is further configured to:
receive a user input modifying the identified aspects term.
17. The system of claim 13 , wherein the processor is further configured to:
receive a user input modifying the identified polarity of the aspects term.
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 the aspect term represents one of: a function of the target entity or a component of the target entity.
20. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:
receive a custom dictionary comprising a list of lexemes referencing at least one of: a target entity or an aspect associated with the target entity;
perform, using the custom dictionary, 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 plurality of syntactico-semantic structures to detect, within the part of the natural language text, an aspect term representing an aspect associated with a target entity;
identify, in the plurality of syntactico-semantic structures, a highest constituent having a kernel comprised by the aspect term;
evaluate a classifier function determine a polarity associated with the aspect term, wherein a domain of the classifier function comprises one or more attributes of a context of the highest constituent; and
generate a report comprising the aspect term and the polarity of the aspect term.