Abstract
In this study, we describe a text processing pipeline that transforms user-generated text into structured data. To do this, we train neural and transformer-based models for aspect-based sentiment analysis. As most research deals with explicit aspects from product or service data, we extract and classify implicit and explicit aspect phrases from German-language physician review texts. Patients often rate on the basis of perceived friendliness or competence. The vocabulary is difficult, the topic sensitive, and the data user-generated. The aspect phrases come with various wordings using insertions and are not noun-based, which makes the presented case equally relevant and reality-based. To find complex, indirect aspect phrases, up-to-date deep learning approaches must be combined with supervised training data. We describe three aspect phrase datasets, one of them new, as well as a newly annotated aspect polarity dataset. Alongside this, we build an algorithm to rate the aspect phrase importance. All in all, we train eight transformers on the new raw data domain, compare 54 neural aspect extraction models and, based on this, create eight aspect polarity models for our pipeline. These models are evaluated by using Precision, Recall, and F-Score measures. Finally, we evaluate our aspect phrase importance measure algorithm.
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
Similar content being viewed by others
Notes
- 1.
https://ratemds.com, accessed: 2020-12-17.
- 2.
https://jameda.de, accessed: 2020-12-17.
- 3.
Jameda: https://jameda.de; Docfinder: https://docfinder.at; Medicosearch: https://medicosearch.ch; accessed 2021-01-11.
- 4.
Translated from German, with the team as the aspect target: “Betreuung/Engagement”, “Freundlichkeit”, “Kompetenz”, and “Telefonerreichbarkeit”.
References
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. ACL 5, 135–146 (2017)
Chinsha, T.C., Shibily, J.: A syntactic approach for aspect based opinion mining. In: Proceedings of the 9th IEEE International Conference on Semantic Computing, pp. 24–31. IEEE (2015)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the ACL, pp. 8440–8451. ACL, Online (2020)
Cordes, M.: Wie bewerten die anderen? Eine übergreifende Analyse von Arztbewertungsportalen in Europa. Master’s thesis, Paderborn University (2018)
De Clercq, O., Lefever, E., Jacobs, G., Carpels, T., Hoste, V.: Towards an integrated pipeline for aspect-based sentiment analysis in various domains. In: Proceedings of the 8th ACL Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 136–142. ACL (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the ACL: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. ACL (2019)
Kersting, J., Geierhos, M.: Aspect phrase extraction in sentiment analysis with deep learning. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence: Special Session on Natural Language Processing in Artificial Intelligence, pp. 391–400. SCITEPRESS (2020)
Kersting, J., Geierhos, M.: Neural learning for aspect phrase extraction and classification in sentiment analysis. In: Proceedings of the 33rd International FLAIRS, pp. 282–285. AAAI (2020)
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. SCI, vol. 939, pp. 163–189. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-63787-3_6
Krippendorff, K.: Computing Krippendorff’s Alpha-Reliability. Technical report 1–25-2011, University of Pennsylvania (2011)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Liu, Y., Bi, J.W., Fan, Z.P.: Ranking products through online reviews: a method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf. Fusion 36, 149–161 (2017)
López, A., Detz, A., Ratanawongsa, N., Sarkar, U.: What patients say about their doctors online: a qualitative content analysis. J. General Internal Med. 27(6), 685–692 (2012)
Nazir, A., Rao, Y., Wu, L., Sun, L.: Issues and challenges of aspect-based sentiment analysis: a comprehensive survey. IEEE Trans. Affective Comput. 1 (2020). https://doi.org/10.1109/TAFFC.2020.2970399
Nguyen, T.H., Shirai, K.: PhraseRNN: phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2509–2514. ACL (2015)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation, pp. 19–30. ACL (2016)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2016 Task 5: Aspect Based Sentiment Analysis (ABSA-16) Annotation Guidelines (2016)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation, pp. 27–35. ACL (2014)
Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 486–495. ACL (2015)
Sharma, R., Somani, A., Kumar, L., Bhattacharyya, P.: Sentiment intensity ranking among adjectives using sentiment bearing word embeddings. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 547–552. ACL (2017)
Shrestha, M.: Development of a language model for medical domain. Master’s thesis, Rhine-Waal University of Applied Sciences (2021)
Wojatzki, M., Ruppert, E., Holschneider, S., Zesch, T., Biemann, C.: GermEval 2017: shared task on aspect-based sentiment in social media customer feedback. In: Proceedings of the GermEval 2017 - Shared Task on Aspect-based Sentiment in Social Media Customer Feedback, pp. 1–12. Springer (2017)
Zhang, L., Wang, S., Liu, B.: Deep learning for sentiment analysis: a survey. Wiley Interdisc. Rev.: Data Mining Knowl. Discov. 8(4), 1–25 (2018)
Zhou, J., Huang, J.X., Chen, Q., Hu, Q.V., Wang, T., He, L.: Deep learning for aspect-level sentiment classification: survey, vision, and challenges. IEEE Access 7, 78454–78483 (2019)
Acknowledgments
This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Center On-The-Fly Computing (SFB 901). We thank F. S. Bäumer, M. Cordes, and R. R. Mülfarth for their assistance with the data collection.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Kersting, J., Geierhos, M. (2021). Human Language Comprehension in Aspect Phrase Extraction with Importance Weighting. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-80599-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80598-2
Online ISBN: 978-3-030-80599-9
eBook Packages: Computer ScienceComputer Science (R0)