Exploring a Supervised Learning Based Social Media Business Sentiment Index

Hyeonseo Lee, Harim Seo, Nakyeong Lee, Min Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Emerging Databases - Technologies, Applications, and Theory
EditorsWonik Choi, Wookey Lee, Min Song, Sungwon Jung
PublisherSpringer Verlag
Pages193-202
Number of pages10
ISBN (Print)9789811065194
DOIs
Publication statusPublished - 2018
Event7th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2017 - Busan, Korea, Republic of
Duration: 2017 Aug 72017 Aug 9

Publication series

NameLecture Notes in Electrical Engineering
Volume461
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

Other7th International Conference on Emerging Databases: Technologies, Applications, and Theory, EDB 2017
CountryKorea, Republic of
CityBusan
Period17/8/717/8/9

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Exploring a Supervised Learning Based Social Media Business Sentiment Index'. Together they form a unique fingerprint.

  • Cite this

    Lee, H., Seo, H., Lee, N., & Song, M. (2018). Exploring a Supervised Learning Based Social Media Business Sentiment Index. In W. Choi, W. Lee, M. Song, & S. Jung (Eds.), Proceedings of the 7th International Conference on Emerging Databases - Technologies, Applications, and Theory (pp. 193-202). (Lecture Notes in Electrical Engineering; Vol. 461). Springer Verlag. https://doi.org/10.1007/978-981-10-6520-0_20