Systems and methods for aspect-based opinion mining including identifying the polarity (e.g., positive, negative, etc.) of different features of a product or a service as expressed in a text. This general task can be divided into four sub-tasks: identifying the aspect terms, classifying them into one of a set of predefined aspect categories, and identifying the polarity of the aspects terms and the aspect categories. A combination of systems (e.g., rule-based and machine learning based) can be employed to implement aspect category and aspect term polarity mutual annotation for aspect-based sentiment analysis.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for aspect-based sentiment analysis for opinion mining, said system comprising: a sentiment detection module based on deep syntactic parsing; and a plurality of machine learning classification components that communicates with said sentiment detection module and which processes data provided by said sentiment detection module to determine sentiments expressed with respect to varying aspects of a domain. 2. The system of claim 1 wherein said sentiment detection module comprises a syntactic parsing component that detects relevant aspect terms and aspect categories with respect to a domain. 3. The system of claim 1 wherein said plurality of machine learning classification components further comprises a sentence classification module that associates aspect categories to sentences. 4. The system of claim 3 wherein said sentiment detection module further comprises a sentiment grammar component to associate polarities to aspect terms and aspect categories. 5. The system of claim 3 wherein said plurality of machine learning classification components further comprises a classification module that associates polarities to aspect categories detected by said sentence classification module. 6. The system of claim 1 wherein said plurality of machine learning classification components further comprises a polarity correction module that corrects polarities of aspect terms using data indicative of aspect category polarity classification. 7. The system of claim 1 wherein said sentiment detection module comprises an RBS (Rule-Based System) that detects fine-grained information. 8. The system of claim 3 wherein said plurality of machine learning classification components further comprises a polarity correction module that corrects polarities of aspect terms using data indicative of aspect category polarity classification. 9. The system of claim 4 wherein said sentiment detection module comprises an RBS (Rule-Based System) that detects fine-grained information. 10. The system of claim 9 wherein said RBS is configured such that if no polarities are associated to an aspect term, said aspect term is associated by default with a neutral polarity. 11. A system for aspect-based sentiment analysis for opinion mining, said system comprising: at least one processor; and a computer-usable medium embodying computer program code, said computer-usable medium capable of communicating with said at least one processor, said computer program code comprising instructions executable by said at least one processor and configured to: detect via a syntactic parsing module relevant aspect terms and aspect categories with respect to a domain; associate aspect categories to sentences via a sentence classification module; associate via a sentiment grammar component polarities to aspect terms and aspect categories detected by said syntactic parsing module; associate via a classification module polarities to aspect categories detected by said sentence classification module; and correct polarities of aspect terms via a polarity correction module using data indicative of aspect category polarity classification. 12. A method for aspect-based sentiment analysis for opinion mining, said method comprising: providing a sentiment detection module based on deep syntactic parsing; and configuring a plurality of machine learning classification components, which communicate with said sentiment detection module and which processes data provided by said sentiment detection module to determine sentiments expressed with respect to varying aspects of a domain. 13. The method of claim 12 further comprising configuring said sentiment detection module to include a syntactic parsing component that detects relevant aspect terms and aspect categories with respect to a domain. 14. The method of claim 12 further comprising configuring said plurality of machine learning classification components to further include a sentence classification module that associates aspect categories to sentences. 15. The method of claim 14 further comprising configuring said sentiment detection module to further include a sentiment grammar component to associate polarities to aspect terms and aspect categories. 16. The method of claim 14 further comprising configuring said plurality of machine learning classification components to further include a classification module that associates polarities to aspect categories detected by said sentence classification module. 17. The method of claim 12 further comprising configuring said plurality of machine learning classification components to further include a polarity correction module that corrects polarities of aspect terms using data indicative of aspect category polarity classification. 18. The method of claim 12 further comprising configuring said sentiment detection module to include an RBS (Rule-Based System) that detects fine-grained information. 19. The method of claim 14 further comprising configuring said plurality of machine learning classification components to further include a polarity correction module that corrects polarities of aspect terms using data indicative of aspect category polarity classification. 20. The method of claim 15 further comprising: configuring said sentiment detection module to further include an RBS (Rule-Based System) that detects fine-grained information, wherein said RBS is configured such that if no polarities are associated to an aspect term, said aspect term is associated by default with a neutral polarity.