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Teamfoto vom 23.11.2017 (Foto: Universität Paderborn, Astrid van Kimmenade)

Joschka Kersting, M.Sc.

Kontakt
Vita
Publikationen
 Joschka Kersting, M.Sc.

Digitale Kulturwissenschaften

Wissenschaftlicher Mitarbeiter - AG Semantische Informationsverarbeitung

Telefon:
+49 5251 60-5669
Fax:
+49 5251 60-5672
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W2.108
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Besucher:
Mersinweg 3
33100 Paderborn

04/2018 - heute

Wissenschaftlicher Mitarbeiter an der Professur für Digitale Kulturwissenschaften

Universität Paderborn

12/2017 - 03/2018

Wissenschaftliche Hilfskraft an der Professur für Digitale Kulturwissenschaften

Universität Paderborn

10/2015 - 03/2018

Masterstudium Management Information Systems

Universität Paderborn

04/2016 - 11/2017

Wissenschaftliche Hilfskraft an der Juniorprofessur für Wirtschaftsinformatik, insb. Semantische Informationsverarbeitung

Heinz Nixdorf Institut
Universität Paderborn

10/2016 - 12/2016

Forschungsaufenthalt am KISTI

Korea Institute of Science and Technology Information (KISTI)
Daejeon, Republik Korea (Südkorea)

10/2012 - 09/2015

Bachelorstudium International Business

Fachhochschule der Wirtschaft, Paderborn


Liste im Research Information System öffnen

2020

Neural Learning for Aspect Phrase Extraction and Classification in Sentiment Analysis

J. Kersting, M. Geierhos, in: Proceedings of the 33rd International Florida Artificial Intelligence Research Symposium (FLAIRS) Conference, AAAI, 2020


What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers

J. Kersting, M. Geierhos, in: Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods, SCITEPRESS, 2020

This paper deals with online customer reviews of local multi-service providers. While many studies investigate product reviews and online labour markets with service providers delivering intangible products “over the wire”, we focus on websites where providers offer multiple distinct services that can be booked, paid and reviewed online but are performed locally offline. This type of service providers has so far been neglected in the literature. This paper analyses reviews and applies sentiment analysis. It aims to gain new insights into local multi-service providers’ performance. There is a broad literature range presented with regard to the topics addressed. The results show, among other things, that providers with good ratings continue to perform well over time. We find that many positive reviews seem to encourage sales. On average, quantitative star ratings and qualitative ratings in the form of review texts match. Further results are also achieved in this study.


Aspect Phrase Extraction in Sentiment Analysis with Deep Learning

J. Kersting, M. Geierhos, in: Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020), SCITEPRESS, 2020

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.


Detection of Privacy Disclosure in the Medical Domain: A Survey

B. Buff, J. Kersting, M. Geierhos, in: Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), SCITEPRESS, 2020

When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.


2019

Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches

F.S. Bäumer, J. Kersting, M. Geierhos, Computers (2019), 8(1)

The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances.


In Reviews We Trust: But Should We? Experiences with Physician Review Websites

J. Kersting, F.S. Bäumer, M. Geierhos, in: Proceedings of the 4th International Conference on Internet of Things, Big Data and Security, SCITEPRESS, 2019, pp. 147-155

The ability to openly evaluate products, locations and services is an achievement of the Web 2.0. It has never been easier to inform oneself about the quality of products or services and possible alternatives. Forming one’s own opinion based on the impressions of other people can lead to better experiences. However, this presupposes trust in one’s fellows as well as in the quality of the review platforms. In previous work on physician reviews and the corresponding websites, it was observed that there occurs faulty behavior by some reviewers and there were noteworthy differences in the technical implementation of the portals and in the efforts of site operators to maintain high quality reviews. These experiences raise new questions regarding what trust means on review platforms, how trust arises and how easily it can be destroyed.


2018

Towards a Multi-Stage Approach to Detect Privacy Breaches in Physician Reviews

F.S. Bäumer, J. Kersting, M. Orlikowski, M. Geierhos, in: Proceedings of the Posters and Demos Track of the 14th International Conference on Semantic Systems co-located with the 14th International Conference on Semantic Systems (SEMANTiCS 2018), CEUR-WS.org, 2018

Physician Review Websites allow users to evaluate their experiences with health services. As these evaluations are regularly contextualized with facts from users’ private lives, they often accidentally disclose personal information on the Web. This poses a serious threat to users’ privacy. In this paper, we report on early work in progress on “Text Broom”, a tool to detect privacy breaches in user-generated texts. For this purpose, we conceptualize a pipeline which combines methods of Natural Language Processing such as Named Entity Recognition, linguistic patterns and domain-specific Machine Learning approaches which have the potential to recognize privacy violations with wide coverage. A prototypical web application is openly accesible.


Rate Your Physician: Findings from a Lithuanian Physician Rating Website

F.S. Bäumer, J. Kersting, V. Kuršelis, M. Geierhos, in: Communications in Computer and Information Science, Springer, 2018, pp. 43-58

Physician review websites are known around the world. Patients review the subjectively experienced quality of medical services supplied to them and publish an overall rating on the Internet, where quantitative grades and qualitative texts come together. On the one hand, these new possibilities reduce the imbalance of power between health care providers and patients, but on the other hand, they can also damage the usually very intimate relationship between health care providers and patients. Review websites must meet these requirements with a high level of responsibility and service quality. In this paper, we look at the situation in Lithuania: Especially, we are interested in the available possibilities of evaluation and interaction, and the quality of a particular review website measured against the available data. We thereby identify quality weaknesses and lay the foundation for future research.



2017

Using Sentiment Analysis on Local Up-to-the-Minute News: An Integrated Approach

J. Kersting, M. Geierhos, in: Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania, October 12–14, 2017, Proceedings, Springer, 2017, pp. 528-538

In this paper, we present a search solution that makes local news information easily accessible. In the era of fake news, we provide an approach for accessing news information through opinion mining. This enables users to view news on the same topics from different web sources. By applying sentiment analysis on social media posts, users can better understand how issues are captured and see people’s reactions. Therefore, we provide a local search service that first localizes news articles, then visualizes their occurrence according to the frequency of mentioned topics on a heatmap and even shows the sentiment score for each text.


Privacy Matters: Detecting Nocuous Patient Data Exposure in Online Physician Reviews

F.S. Bäumer, N. Grote, J. Kersting, M. Geierhos, in: Information and Software Technologies: 23rd International Conference, ICIST 2017, Druskininkai, Lithuania, October 12–14, 2017, Proceedings, Springer, 2017, pp. 77-89

Consulting a physician was long regarded as an intimate and private matter. The physician-patient relationship was perceived as sensitive and trustful. Nowadays, there is a change, as medical procedures and physicians consultations are reviewed like other services on the Internet. To allay user’s privacy doubts, physician review websites assure anonymity and the protection of private data. However, there are hundreds of reviews that reveal private information and hence enable physicians or the public to identify patients. Thus, we draw attention to the cases when de-anonymization is possible. We therefore introduce an approach that highlights private information in physician reviews for users to avoid an accidental disclosure. For this reason, we combine established natural-language-processing techniques such as named entity recognition as well as handcrafted patterns to achieve a high detection accuracy. That way, we can help websites to increase privacy protection by recognizing and uncovering apparently uncritical information in user-generated texts.


Internet of Things Architecture for Handling Stream Air Pollution Data

J. Kersting, M. Geierhos, H. Jung, T. Kim, in: Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, SCITEPRESS, 2017, pp. 117-124

In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.


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