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

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Natural Language Processing in Artificial Intelligence—NLPinAI 2020

Part of the book series: Studies in Computational Intelligence ((SCI,volume 939))

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.

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Notes

  1. 1.

    http://ratemds.com, last visit was on 2020-05-19.

  2. 2.

    http://jameda.de, last visit was on 2020-05-19.

  3. 3.

    http://pincetas.lt, last visit was on 2020-05-19.

  4. 4.

    http://docfinder.at, last visit was on 2020-05-19.

  5. 5.

    http://medicosearch.ch, last visit was on 2020-05-19.

  6. 6.

    The aspect classes were translated from German: Freundlichkeit, Kompetenz, Zeit genommen, Aufkärung.

  7. 7.

    The aspect classes were originally in German: Behandlung, Alternativheilmethoden, Vertrauensverhältnis, Kinderfreundlichkeit, Betreuung/Engagement, Gesamt/Empfehlung.

  8. 8.

    The neural network was inspired by [37].

  9. 9.

    The scheme was as follows: sentence: 1, classes predicted: [1]/ sentence: 2, classes predicted: [2, 5]/ ...; sentence: 7, classes predicted: [1]/ etc.

  10. 10.

    SpatialDropout was used only for the first dataset (fkza) and the biLSTM-CRF with FastText embeddings, while we relied on a regular dropout layer for the other cases. While SpatialDropout performed slightly better in this case, the overall effect was marginal and we thus trusted the normal Dropout, which is more suitable for sequence processing in general. The difference between Dropout and SpatialDropout is that the latter drops whole feature maps instead of single elements [72].

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Acknowledgements

This study is an invited, extended work based on [35]. Another related study is [36], which was written and submitted during the same period as [35]. This work was partially supported by the German Research Foundation (DFG) within the Collaborative Research Centre On-The-Fly Computing (SFB 901). We thank Rieke Roxanne Mülfarth, Frederik Simon Bäumer and Marvin Cordes for their support with the data collection.

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  1. Paderborn University, Warburger Str. 100, Paderborn, Germany

    Joschka Kersting

  2. Bundeswehr University Munich, Research Institute CODE, Carl-Wery-Straße 22, Munich, Germany

    Michaela Geierhos

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    Roussanka Loukanova

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Kersting, J., Geierhos, M. (2021). Towards Aspect Extraction and Classification for Opinion Mining with Deep Sequence Networks. In: Loukanova, R. (eds) Natural Language Processing in Artificial Intelligence—NLPinAI 2020. Studies in Computational Intelligence, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-63787-3_6

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