Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning
| Autor: | Ayu Purwarianti, Fariska Zakhralativa Ruskanda, Dwi H. Widyantoro |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Dependency (UML) General Computer Science Computer science Property (programming) rule learning dependency rule TK5101-6720 Information technology 02 engineering and technology computer.software_genre Set (abstract data type) 030507 speech-language pathology & audiology 03 medical and health sciences sequential covering 0202 electrical engineering electronic engineering information engineering aspect extraction Electrical and Electronic Engineering Baseline (configuration management) Sentiment analysis aspect-based sentiment analysis T58.5-58.64 Task (computing) Product (mathematics) Metric (mathematics) Telecommunication 020201 artificial intelligence & image processing Data mining 0305 other medical science computer |
| Zdroj: | Journal of ICT Research and Applications, Vol 14, Iss 1 (2020) |
| ISSN: | 2338-5499 2337-5787 |
| DOI: | 10.5614/itbj.ict.res.appl.2020.14.1.4 |
| Popis: | The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|