We investigate the relationship between public sentiment on specific issues including Sewol ferry disaster and the outbreak of MERS and economic performance in Korea. For this work, we implement two tasks: (1) after training sentiment classifiers with several sources of social media datasets, we consider three kinds of feature sets such as feature vector, sequence vector and combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers including MaxEnt-L1, C4.5 Decision Tree, SVM-kernel, Ada-boost, Naïve Bayes and Max Ent is assessed. Hence, MaxEnt-L1 shows the highest performance amongst other classifiers. (2) we collect datasets pertinent to a number of critical events that public explicitly express their opinions on such as Sewol ferry disaster, the outbreak of MERS, etc. Then, we calculate sentiment value of the collected data with trained classifiers and compare it with economic indices.
|Title of host publication||Proceedings of the 7th International Conference on Emerging Databases - Technologies, Applications, and Theory|
|Editors||Wonik Choi, Wookey Lee, Min Song, Sungwon Jung|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||7th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2017 - Busan, Korea, Republic of|
Duration: 2017 Aug 7 → 2017 Aug 9
|Name||Lecture Notes in Electrical Engineering|
|Other||7th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2017|
|Country||Korea, Republic of|
|Period||17/8/7 → 17/8/9|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046711).
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering