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Use your smartphone to scan this QR code and download this article ABSTRACT Social media is emerging as a popular channel for online marketing. Nowadays, there more and more brands those are using social media to track and care for their brand health. Especially, social media is a source and also an important channel for brands to take care of their brands. On social media, things can move quickly due to viral information spread among the audience. Thus, a robust and automatic method for detecting crisis and even stop the crisis before it starts is urgently demanded.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, whereas the sentiment analysis process helps to identify the opinion of the audience regarding the brands. By combining domain knowledge captured in the ontology, we can make the analysis process focused on certain domains when needed. Also, the ontological concepts can also improve the accuracy of sentiment analysis at the aspect level.To evaluate the performance of our approach, we collect real data from online social media channels in Vietnam, which are provided by YouNetMedia, a professional online data analysis company. 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. Based on the achieved results, commercial products and platforms can be seriously considered.

Ontology-based Sentiment Analysis for Brand Crisis Detection on Online Social Media