| Summary |
In recent years, people have used social media networks frequently to express their opinions because of vigorous development of social media. Therefore, social media mining with big data analytics can be applied to overview public opinions and sentiment tendencies for the purpose of intelligent decision making.
This study aims at establishing an internet public opinion analysis model with text mining technologies. Firstly, social media comments on popular websites are collected. Text classification approaches are then used to divide TRA‘s (Taiwan Railway Administration) service related comments into "station facilities", "employee rights", "catering services", "tickets system" and "train operation" types of topics.And then emotional values of daily comments on these five topics are calculated based on sentiment analysis. Finally, safety performance data provided by TRA (I.e. incident data) are chosen for mapping with internet public opinion results.
Empirical results showed that only "train operation" is the most significant category in the correlation with safety performance. And the lowest average sentiment score is "tickets system" category. Although "catering services" category shows positive comments, its average sentiment score is negative. It indicates that TRA’s services need more active improvements.
A comparison chart can be visualized to map TRA’s safety performance with internet public opinions. It is proven to be helpful for TRA’s decision makers to monitor public opinion changes by using social media mining when accidents or incidents happen. |
