@inproceedings{31054,
  abstract     = {{This paper aims at discussing past limitations set in sentiment analysis research regarding explicit and implicit mentions of opinions. Previous studies have regularly neglected this question in favor of methodical research on standard-datasets. Furthermore, they were limited to linguistically less-diverse domains, such as commercial product reviews. We face this issue by annotating a German-language physician review dataset that contains numerous implicit, long, and complex statements that indicate aspect ratings, such as the physician’s friendliness. We discuss the nature of implicit statements and present various samples to illustrate the challenge described.}},
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon}},
  booktitle    = {{Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications}},
  editor       = {{Kersting, Joschka}},
  keywords     = {{Sentiment analysis, Natural language processing, Aspect phrase extraction}},
  location     = {{Barcelona, Spain}},
  pages        = {{5--9}},
  publisher    = {{IARIA}},
  title        = {{{Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis}}},
  year         = {{2022}},
}

@inproceedings{15580,
  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.}},
  author       = {{Kersting, Joschka and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)}},
  keywords     = {{Deep Learning, Natural Language Processing, Aspect-based Sentiment Analysis}},
  location     = {{Valetta, Malta}},
  pages        = {{391----400}},
  publisher    = {{SCITEPRESS}},
  title        = {{{Aspect Phrase Extraction in Sentiment Analysis with Deep Learning}}},
  year         = {{2020}},
}

@inproceedings{15256,
  abstract     = {{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.}},
  author       = {{Kersting, Joschka and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods}},
  keywords     = {{Customer Reviews, Sentiment Analysis, Online Labour Markets}},
  location     = {{Valetta, Malta}},
  pages        = {{263----272}},
  publisher    = {{SCITEPRESS}},
  title        = {{{What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers}}},
  year         = {{2020}},
}

@inproceedings{1135,
  abstract     = {{In this paper, we describe our system developed for the GErman SenTiment AnaLysis shared Task (GESTALT) for participation in the Maintask 2: Subjective Phrase and Aspect Extraction from Product Reviews. We present a tool, which identifies subjective and aspect phrases in German product reviews. For the recognition of subjective phrases, we pursue a lexicon-based approach. For the extraction of aspect phrases from the reviews, we consider two possible ways: Besides the subjectivity and aspect look-up, we also implemented a method to establish which subjective phrase belongs to which aspect. The system achieves better results for the recognition of aspect phrases than for the subjective identification.}},
  author       = {{Dollmann, Markus and Geierhos, Michaela}},
  booktitle    = {{Workshop Proceedings of the 12th Edition of the KONVENS Conference}},
  editor       = {{Faaß, Gertrud and Ruppenhofer, Josef}},
  isbn         = {{978-3-934105-47-8}},
  keywords     = {{corpus linguistics, sentiment analysis}},
  location     = {{Hildesheim, Germany}},
  pages        = {{185--191}},
  publisher    = {{Universitätsverlag Hildesheim}},
  title        = {{{SentiBA: Lexicon-based Sentiment Analysis on German Product Reviews}}},
  year         = {{2014}},
}

@article{4696,
  author       = {{vom Brocke, Jan and Debortoli, Stefan and Reuter, Nadine and Müller, Oliver}},
  issn         = {{15293181}},
  journal      = {{Communications of the Association for Information Systems}},
  keywords     = {{Advanced business analytics, Big Data, Business intelligence, IT business value, In-memory technology, OLAP, OLTP, Realtime analytics, Sentiment analysis}},
  pages        = {{151----167}},
  title        = {{{How In-Memory Technology Can Create Business Value: Lessons Learned from Hilti}}},
  doi          = {{10.17705/1CAIS.03407}},
  year         = {{2014}},
}

@inproceedings{13322,
  abstract     = {{Previous research suggests the existence of sentiments in online social networks. In comparison to real life human interaction, in which sentiments have been shown to have an influence on human behaviour, it is not yet completely understood which mechanisms explain how sentiments influence users in online environments. We develop a theoretical framework that tries to bridge the gap between social influence theories that focus on offline interactions on one hand and online interaction in social networks on the other hand. We then test our hypothesis about the influence and dissemination of sentiments in a quantitative analysis that is based on retrieved textual messages of communication patterns in over 12000 online social networks. Our empirical results suggest a general influence of sentiments on node communication patterns that is evidenced by increased occurrences of subsequent messages that express the same sentiment polarization. We interpret these findings and suggest future research to advance our currently limited theories that assume perceived and generalized social influence to path-dependent social influence models that consider actual behaviour.}},
  author       = {{Hillmann, Robert and Trier, Matthias}},
  booktitle    = {{ECIS 2013 Proceedings}},
  isbn         = {{9783834924421}},
  keywords     = {{Social Network Analysis, Sentiment Analysis, Communication Patterns}},
  publisher    = {{Association for Information Systems. AIS Electronic Library (AISeL)}},
  title        = {{{Influence and Dissemination Of Sentiments in Social Network Communication Patterns}}},
  year         = {{2013}},
}