@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}},
}

@article{4691,
  abstract     = {{Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others.}},
  author       = {{Debortoli, Stefan and Müller, Oliver and Junglas, Iris and vom Brocke, Jan}},
  isbn         = {{9781615679119}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the Association for Information Systems}},
  keywords     = {{Latent dirichlet allocation, Online customer reviews, Text mining, Topic modeling, User satisfaction}},
  pages        = {{555--582}},
  title        = {{{Text Mining for Information Systems Researchers: An Annotated Tutorial}}},
  doi          = {{10.17705/1CAIS.03907}},
  year         = {{2016}},
}