Mining User-Generated Repair Instructions from Automotive Web Communities

Date
2019-01-08
Authors
Wambsganss, Thiemo
Fromm, Hansjörg
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The objective of this research was to automatically extract user-generated repair instructions from large amounts of web data. An artifact has been created that classifies a web post as containing a repair instruction or not. Methods from Natural Language Processing are used to transform the unstructured textual information from a web post into a set of numerical features that can be further processed by different Machine Learning Algorithms. The main contribution of this research lies in the design and prototypical implementation of these features. The evaluation shows that the created artifact can accurately distinguish posts containing repair instructions from other posts e.g. containing problem reports. With such a solution, a company can save a lot of time and money that was previously necessary to perform this classification task manually.
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Data, Text, and Web Mining for Business Analytics, Decision Analytics, Mobile Services, and Service Science, Text Mining, Machine Learning, User-Generated Content, Automotive Web Communities, Repair Instructions
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10 pages
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Proceedings of the 52nd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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