Abstract
In this paper, we propose the use of deep contextualised word embeddings to semi-automatically build a domain sentiment ontology. Compared to previous research, we use deep contextualised word embeddings to better cope with various meanings of words. A state-of-the-art hybrid method is used for aspect-based sentiment analysis, called HAABSA++, to evaluate our obtained ontology on the SemEval-2016 restaurant dataset. We achieve a prediction accuracy of 81.85% for the hybrid model with our ontology, which outperforms the hybrid model with other considered ontologies. Furthermore, we find that the ontology obtained from our proposed domain sentiment ontology builder, called DCWEB-SOBA, on itself improves the accuracy for the conclusive cases from 83.04% to 84.52% compared to the ontology builder based on non-contextual word embeddings, WEB-SOBA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Behnke, L.: (2012). https://github.com/lbehnke/hierarchical-clustering-java
Blaschke, C., Valencia, A.: Automatic ontology construction from the literature. Genome Inform. 13, 201–213 (2002)
Bleier, S.: (2000). https://gist.github.com/sebleier/554280
Dera, E., Frasincar, F., Schouten, K., Zhuang, L.: SASOBUS: semi-automatic sentiment domain ontology building using Synsets. In: Harth, A., et al. (eds.) ESWC 2020. LNCS, vol. 12123, pp. 105–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49461-2_7
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), pp. 4171–4186. ACL (2019)
Dragoni, M., Donadello, I., Cambria, E.: OntoSenticNet 2: Enhancing reasoning within sentiment analysis. IEEE Intell. Syst. 37(1) (2022)
Dragoni, M., Tettamanzi, A., da Costa Pereira, C.: DRANZIERA: an evaluation protocol for multi-domain opinion mining. In: 10th International Conference on Language Resources and Evaluation (LREC 2016), pp. 267–272. ELRA (2016)
Gugger, S., Howard, J.: AdamW and super-convergence is now the fastest way to train neural nets. fast.ai (2018)
ten Haaf, F., et al.: WEB-SOBA: word embeddings-based semi-automatic ontology building for aspect-based sentiment classification. In: Verborgh, R., et al. (eds.) ESWC 2021. LNCS, vol. 12731, pp. 340–355. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77385-4_20
Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations (ICLR 2019). OpenReview.net (2019)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), pp. 3111–3119. Curran Associates (2013)
Peters, M.E., et al.: Deep contextualized word representations. In: 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018), pp. 2227–2237. ACL (2018)
Pontiki, M., et al.: SemEval-2016 task 5: aspect-based sentiment analysis. In: 10th International Workshop on Semantic Evaluation (SemEval 2016), pp. 19–30. ACL (2016)
Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)
Schouten, K., Frasincar, F.: Ontology-driven sentiment analysis of product and service aspects. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 608–623. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_39
Schouten, K., Frasincar, F., de Jong, F.: Ontology-enhanced aspect-based sentiment analysis. In: Cabot, J., De Virgilio, R., Torlone, R. (eds.) ICWE 2017. LNCS, vol. 10360, pp. 302–320. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60131-1_17
Toutanova, K., Manning, C.D.: Enriching the knowledge sources used in a maximum entropy part-of-speech tagger. In: 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP 2010), pp. 63–70. ACL (2000)
Truşcǎ, M.M., Wassenberg, D., Frasincar, F., Dekker, R.: A hybrid approach for aspect-based sentiment analysis using deep contextual word embeddings and hierarchical attention. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds.) ICWE 2020. LNCS, vol. 12128, pp. 365–380. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50578-3_25
Wallaart, O., Frasincar, F.: A hybrid approach for aspect-based sentiment analysis using a lexicalized domain ontology and attentional neural models. In: Hitzler, P., Fernández, M., Janowicz, K., Zaveri, A., Gray, A.J.G., Lopez, V., Haller, A., Hammar, K. (eds.) ESWC 2019. LNCS, vol. 11503, pp. 363–378. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21348-0_24
Yelp (2019). https://www.yelp.com/dataset
Zhuang, L., Schouten, K., Frasincar, F.: SOBA: semi-automated ontology builder for aspect-based sentiment analysis. J. Web Semant. 60, 100–544 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
van Lookeren Campagne, R., van Ommen, D., Rademaker, M., Teurlings, T., Frasincar, F. (2022). DCWEB-SOBA: Deep Contextual Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification. In: Groth, P., et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-06981-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06980-2
Online ISBN: 978-3-031-06981-9
eBook Packages: Computer ScienceComputer Science (R0)