WEB-SOBA is a method for Word Embeddings-Based Semi-automatic Ontology Building for Aspect-based sentiment analysis.
This software first requires you to train word2vec vectors on a domain of interest and then use this new word embedding model in the ontology builder. However if you are interested in using WEB-SOBA for the restaurant review domain, you can download the following large files via follow google drive: https://drive.google.com/open?id=19kkxN64GVWqnPKVcCy6a5JseRRb26usu. Add these files to a new folder called "largeData" in src/main/resources. The next step consists of semi-automatically building an ontology from the generated word embeddings. A small bit of user input is required in this ontology building step.
The ontology obtained from WEB-SOBA can be used in aspect-based sentiment classification (ABSA). We recommend the following frameworks for evaluation: Heracles and HAABSA. Heracles is more user friendy, whereas HAABSA provides better results.
Our method compares positvely to other methods for producing an ontology. An overview of the comparison between different ontologies for SemEval Task 5 data is given in the following table:
| Ontology + ML method | Out-of-sample | In-sample | Cross-validation |
|---|---|---|---|
| Manual + LCR-Rot-hop | 86.65% | 87.96% | 82.76% |
| SASOBUS + LCR-Rot-hop | 84.76% | 83.38% | 80.20% |
| SOBA + LCR-Rot-hop | 86.23% | 85.93% | 80.15% |
| WEB-SOBA + LCR-Rot-hop | 87.16% | 88.87% | 84.72% |
Our method WEB-SOBA is related to the following papers:
- Dera, E., Frasincar, F., Schouten, K., Zhuang, L.: Sasobus: Semi-automatic sentiment domain ontology building using synsets. In: European Semantic Web Conference. pp. 105–120. Springer (2020)
- Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. 1st International Conference on Learning Representations (ICLR 2013) (2013)
- Schouten, K., Frasincar, F.: Ontology-driven sentiment analysis of product and service aspects. In: 15th Extended Semantic Web Conference (ESWC 2018). LNCS, vol. 10843, pp. 608–623. Springer (2018)
- Wallaart, O., Frasincar, F.: A hybrid approach for aspect-based sentiment analysis using a lexicalized domain ontology and attentional neural models. In: 16th Extended Semantic Web Conference (ESWC 2019). LNCS, vol. 11503, pp. 363–378. Springer (2019)
- Zhuang, L., Schouten, K., Frasincar, F.: Soba: Semi-automated ontology builder for aspect-based sentiment analysis. Journal of Web Semantics 60, 100–544 (2020)