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SCI."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Understanding customer feedback is becoming a necessity for companies to identify problems and improve their products and services. Text classification and sentiment analysis can play a major role in analyzing this data by using a variety of machine and deep learning approaches. In this work, different transformer-based models are utilized to explore how efficient these models are when working with a German customer feedback dataset. In addition, these pre-trained models are further analyzed to determine if adapting them to a specific domain using unlabeled data can yield better results than off-the-shelf pre-trained models. To evaluate the models, two downstream tasks from the GermEval 2017 are considered. The experimental results show that transformer-based models can reach significant improvements compared to a fastText baseline and outperform the published scores and previous models. For the subtask<jats:italic>Relevance Classification<\/jats:italic>, the best models achieve a micro-averaged<jats:italic>F<\/jats:italic>1-Score of 96.1\u00a0% on the first test set and 95.9\u00a0% on the second one, and a score of 85.1\u00a0% and 85.3\u00a0% for the subtask<jats:italic>Polarity Classification<\/jats:italic>.<\/jats:p>","DOI":"10.1007\/s42979-022-01563-6","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T15:03:55Z","timestamp":1672931035000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Domain Adaptation of Transformer-Based Models Using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1507-9690","authenticated-orcid":false,"given":"Ahmad","family":"Idrissi-Yaghir","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4123-0406","authenticated-orcid":false,"given":"Henning","family":"Sch\u00e4fer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1205-6218","authenticated-orcid":false,"given":"Nadja","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7906-0038","authenticated-orcid":false,"given":"Christoph M.","family":"Friedrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"issue":"3","key":"1563_CR1","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3390\/electronics9030483","volume":"9","author":"NC Dang","year":"2020","unstructured":"Dang NC, Moreno-Garc\u00eda MN, De la Prieta F. 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