Developing a supervised learning-based social media business sentiment index

Hyeonseo Lee, Nakyeong Lee, Harim Seo, Min Song

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The fast-growing digital data generation leads to the emergence of the era of big data, which become particularly more valuable because approximately 70% of the collected data in the world comes from social media. Thus, the investigation of online social network services is of paramount importance. In this paper, we use the sentiment analysis, which detects attitudes and emotions toward issues of society posted in social media, to understand the actual economic situation. To this end, two steps are suggested. In the first step, after training the sentiment classifiers with several big data sources of social media datasets, we consider three types of feature sets: feature vector, sequence vector and a combination of dictionary-based feature and sequence vectors. Then, the performance of six classifiers is assessed: MaxEnt-L1, C4.5 decision tree, SVM-kernel, Ada-boost, Naïve Bayes and MaxEnt. In the second step, we collect datasets that are relevant to several economic words that the public use to explicitly express their opinions. Finally, we use a vector auto-regression analysis to confirm our hypothesis. The results show the statistically significant relationship between public sentiment and economic performance. That is, “depression” and “unemployment” lead to KOSPI. Also, it shows that the extracted keywords from the sentiment analysis, such as “price,” “year-end-tax” and “budget deficit,” cause the exchange rates.

Original languageEnglish
Pages (from-to)3882-3897
Number of pages16
JournalJournal of Supercomputing
Issue number5
Publication statusPublished - 2020 May 1

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A3A2046711).

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Information Systems
  • Hardware and Architecture


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