Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks

J. Kersting, M. Geierhos, in: R. Loukanova (Ed.), Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, Springer, Cham, 2021, pp. 163--189.

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Book Chapter | Published | English
Book Editor
Loukanova, Roussanka
Abstract
This chapter concentrates on aspect-based sentiment analysis, a form of opinion mining where algorithms detect sentiments expressed about features of products, services, etc. We especially focus on novel approaches for aspect phrase extraction and classification trained on feature-rich datasets. Here, we present two new datasets, which we gathered from the linguistically rich domain of physician reviews, as other investigations have mainly concentrated on commercial reviews and social media reviews so far. To give readers a better understanding of the underlying datasets, we describe the annotation process and inter-annotator agreement in detail. In our research, we automatically assess implicit mentions or indications of specific aspects. To do this, we propose and utilize neural network models that perform the here-defined aspect phrase extraction and classification task, achieving F1-score values of about 80% and accuracy values of more than 90%. As we apply our models to a comparatively complex domain, we obtain promising results.
Publishing Year
Book Title
Natural Language Processing in Artificial Intelligence -- NLPinAI 2020
Series Title / Volume
Studies in Computational Intelligence (SCI)
Volume
939
Page
163--189
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Kersting J, Geierhos M. Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks. In: Loukanova R, ed. Natural Language Processing in Artificial Intelligence -- NLPinAI 2020. Vol 939. Studies in Computational Intelligence (SCI). Cham: Springer; 2021:163--189. doi:10.1007/978-3-030-63787-3_6
Kersting, J., & Geierhos, M. (2021). Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks. In R. Loukanova (Ed.), Natural Language Processing in Artificial Intelligence -- NLPinAI 2020 (Vol. 939, pp. 163--189). Cham: Springer. https://doi.org/10.1007/978-3-030-63787-3_6
@inbook{Kersting_Geierhos_2021, place={Cham}, series={Studies in Computational Intelligence (SCI)}, title={Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks}, volume={939}, DOI={10.1007/978-3-030-63787-3_6}, booktitle={Natural Language Processing in Artificial Intelligence -- NLPinAI 2020}, publisher={Springer}, author={Kersting, Joschka and Geierhos, Michaela}, editor={Loukanova, RoussankaEditor}, year={2021}, pages={163--189}, collection={Studies in Computational Intelligence (SCI)} }
Kersting, Joschka, and Michaela Geierhos. “Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks.” In Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, edited by Roussanka Loukanova, 939:163--189. Studies in Computational Intelligence (SCI). Cham: Springer, 2021. https://doi.org/10.1007/978-3-030-63787-3_6.
J. Kersting and M. Geierhos, “Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks,” in Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, vol. 939, R. Loukanova, Ed. Cham: Springer, 2021, pp. 163--189.
Kersting, Joschka, and Michaela Geierhos. “Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks.” Natural Language Processing in Artificial Intelligence -- NLPinAI 2020, edited by Roussanka Loukanova, vol. 939, Springer, 2021, pp. 163--189, doi:10.1007/978-3-030-63787-3_6.
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