||The online electronic media has strong influence on people’s opinions and their daily lives. This is especially true in Taiwan as Taiwan’s internet coverage and usage is among the highest in the world. The opinions on the Internet, therefore, has dramatic impact on the reputation of enterprise and, if not properly handled, may cause social media crisis even for a big company. This research intends to examine the impact of social listening system on enterprise’s emergency reaction mechanism and analyze its effectiveness and the adaptability. |
This thesis chooses to study the case of a semiconductor assembly and test company located in southern Taiwan, which has been using the social listening technology to monitor the electronic media and social media articles whose contents are related to this particular company. All these articles ran through the data modeling, grouping and pattern recognition to detect the abnormal and malicious articles and, if needed, alert top management to conduct decision making and take emergency action.
We conduct qualitative research on analyzing how the social listening system could impact the emergency management of various enterprises in Taiwan.. This research serves three purposes: 1. to identify the functional requirements for the solical listening system, 2. to evaluate the efficiency of social listening analytic system, and 3. to confirm if the social listening system can effectively satisfy the emergency reaction requirement of the target company.
The research results show that all the interviewees express positive opinion on the need of social listening system, and the social listening system has indeed efficiently and effectively satisfy the emergency management requirement. Regarding the requirments of social listening system, real-time alert and prompt reaction tops the needs of the interviewees, which can be satisfied by the functions of alerting, topic clustering, sentiment analysis, and new topic identification.
Keywords: Social Listening, Enterprise Emergency Management Mechanism, Text Mining, Topic Clustering, Sentiment Analysis