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Authors: Dominique Mercier 1 ; Akansha Bhardwaj 2 ; Andreas Dengel 3 and Sheraz Ahmed 2

Affiliations: 1 Technical University Kaiserslautern, Germany ; 2 German Research Center for Artificial Intelligence, Germany ; 3 Technical University Kaiserslautern and German Research Center for Artificial Intelligence, Germany

ISBN: 978-989-758-275-2

Keyword(s): Scientific Document Analysis, Citation Analysis, Sentiment Analysis, Machine Learning.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Natural Language Processing ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: With the rapid growth in the number of scientific publications, year after year, it is becoming increasingly difficult to identify quality authoritative work on a single topic. Though there is an availability of scientometric measures which promise to offer a solution to this problem, these measures are mostly quantitative and rely, for instance, only on the number of times an article is cited. With this approach, it becomes irrelevant if an article is cited 10 times in a positive, negative or neutral way. In this context, it is quite important to study the qualitative aspect of a citation to understand its significance. This paper presents a novel system for sentiment analysis of citations in scientific documents (SentiCite) and is also capable of detecting nature of citations by targeting the motivation behind a citation, e.g., reference to a dataset, reading reference. Furthermore, the paper also presents two datasets (SentiCiteDB and IntentCiteDB) containing about 2,600 citations with their ground truth for sentiment and nature of citation. SentiCite along with other state-of-the-art methods for sentiment analysis are evaluated on the presented datasets. Evaluation results reveal that SentiCite outperforms state-of-the-art methods for sentiment analysis in scientific publications by achieving a F1-measure of 0.71. (More)

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Paper citation in several formats:
Mercier, D.; Bhardwaj, A.; Dengel, A. and Ahmed, S. (2018). SentiCite - An Approach for Publication Sentiment Analysis.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 422-429. DOI: 10.5220/0006587604220429

@conference{icaart18,
author={Dominique Mercier. and Akansha Bhardwaj. and Andreas Dengel. and Sheraz Ahmed.},
title={SentiCite - An Approach for Publication Sentiment Analysis},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={422-429},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006587604220429},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - SentiCite - An Approach for Publication Sentiment Analysis
SN - 978-989-758-275-2
AU - Mercier, D.
AU - Bhardwaj, A.
AU - Dengel, A.
AU - Ahmed, S.
PY - 2018
SP - 422
EP - 429
DO - 10.5220/0006587604220429

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