About: Developing a supervised learning-based social media business sentiment index

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Developing a supervised learning-based social media business sentiment index
Creator	Song, Min Lee, Hyeonseo Lee, Nakyeong Seo, Harim
Source	Medline; PMC
abstract	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.
has issue date	2019-01-10(xsd:dateTime)
bibo:doi	10.1007/s11227-018-02737-x
bibo:pmid	32435085
has license	no-cc
sha1sum (hex)	4d6921cf94516e5507b9de6657f8c417daf9e937
schema:url	https://doi.org/10.1007/s11227-018-02737-x
resource representing a document's title	Developing a supervised learning-based social media business sentiment index
has PubMed Central identifier	PMC7224044
has PubMed identifier	32435085
schema:publication	J Supercomput
resource representing a document's body	covid:4d6921cf94516e5507b9de6657f8c417daf9e937#body_text
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