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Authors: Joschka Kersting and Michaela Geierhos

Affiliation: Semantic Information Processing Group, Paderborn University, Warburger Str. 100, Paderborn, Germany

Keyword(s): Deep Learning, Natural Language Processing, Aspect-based Sentiment Analysis.

Abstract: This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.

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Paper citation in several formats:
Kersting, J. and Geierhos, M. (2020). Aspect Phrase Extraction in Sentiment Analysis with Deep Learning. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 391-400. DOI: 10.5220/0009349903910400

@conference{nlpinai20,
author={Joschka Kersting. and Michaela Geierhos.},
title={Aspect Phrase Extraction in Sentiment Analysis with Deep Learning},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI},
year={2020},
pages={391-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009349903910400},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: NLPinAI
TI - Aspect Phrase Extraction in Sentiment Analysis with Deep Learning
SN - 978-989-758-395-7
IS - 2184-433X
AU - Kersting, J.
AU - Geierhos, M.
PY - 2020
SP - 391
EP - 400
DO - 10.5220/0009349903910400
PB - SciTePress

- Science and Technology Publications, Lda.
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