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This paper discusses detection of brand crisis on online social media, i.e. when a brand is being suffered from unexpectedly high frequency of negative comments on online channels such as social networks, electronic news, blog and forum. In order to do so, we combined the usage of probabilistic model for burst detection with ontology-based aspect-level sentiment analysis technique to detect negative mention. The burst on online environment is a trendy topic that is rapidly growing recently.  Thus, a burst with high frequencies of negative mentions to a brand implies a potential online crisis occurring with that brand. Our experimental results show that the aspect-level sentiment analysis technique is extremely useful for detecting of negative mentions that related with the products and brands.

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Issue: Vol 3 No SI1 (2020): Special Issue: Computer Science and Engineering
Page No.: SI40-SI49
Published: Oct 27, 2020
Section: Research article

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Mai, T., & Quan, T. (2020). Ontology-based Sentiment Analysis for Brand Crisis Detection on Online Social Media. Science & Technology Development Journal - Engineering and Technology, 3(SI1), SI40-SI49.

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