Detecting the outburst of negative electronic word-of-mouth in social media now constitutes a critical managerial priority. However, even though there are several prior researches in detecting the negative electronic word-of-mouth outburst in social media such as Facebook, there is still no well-founded approach for the negative electronic word-of-mouth outburst detection in microblog.
With social media being such a widely used platform, it has quickly made its way into the workplace. As a result, what employee said on their personal social media, might reflects back on the company. By using the algorithm that the reliability has been proven in the prior research as the basis. The researcher able implement further modules that allow access to electronic word-of-mouth generated in microblog using Python programming language to detect the outburst.

Detecting the outburst of negative electronic word-of-mouth in social media now constitutes a critical managerial priority. However, even though there are several prior researches in detecting the negative electronic word-of-mouth outburst in social media such as Facebook, there is still no well-founded approach for the negative electronic word-of-mouth outburst detection in microblog.
With social media being such a widely used platform, it has quickly made its way into the workplace. As a result, what employee said on their personal social media, might reflects back on the company. By using the algorithm that the reliability has been proven in the prior research as the basis. The researcher able implement further modules that allow access to electronic word-of-mouth generated in microblog using Python programming language to detect the outburst.

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