Conceptualizing the use of the term financial risk by non-academics and academics using twitter messages and ScienceDirect paper abstracts

A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter ™ and ScienceDirect ™ —was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communic...

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Vydáno v:	Social network analysis and mining Ročník 11; číslo 1; s. 6
Hlavní autoři:	Kwak, Eun Jin, Grable, John E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.12.2021 Springer Nature B.V
Témata:	Abstracts Academic staff Application programming interface Applications of Graph Theory and Complex Networks Computer Science Concept formation Data analysis Data mining Data Mining and Knowledge Discovery Economics Education Emotions Game Theory Humanities Law Library associations Mass murders Medical libraries Methodology of the Social Sciences Negative emotions Original Original Article Perceptions Policy making Researcher subject relations Risk Risk behavior Risk communication Risk taking Scientific papers Social and Behav. Sciences Social networks Statistics for Social Sciences Teachers Verbal communication Sentiment analysis Twitter and ScienceDirect mining Semantic analysis Financial risk
ISSN:	1869-5450, 1869-5469
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Abstract	A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter ™ and ScienceDirect ™ —was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term—all of which tend to focus predominately on the business and technology aspects of risk taking—the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes.
AbstractList	A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter™ and ScienceDirect™—was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term—all of which tend to focus predominately on the business and technology aspects of risk taking—the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes. A text mining technique, based on an Application Programming Interface (API) request-using narrative data from Twitter™ and ScienceDirect™-was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term-all of which tend to focus predominately on the business and technology aspects of risk taking-the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes.A text mining technique, based on an Application Programming Interface (API) request-using narrative data from Twitter™ and ScienceDirect™-was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term-all of which tend to focus predominately on the business and technology aspects of risk taking-the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes. A text mining technique, based on an Application Programming Interface (API) request-using narrative data from Twitter and ScienceDirect -was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term in their communications. It was determined that unlike the day-to-day uses of the term-all of which tend to focus predominately on the business and technology aspects of risk taking-the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes. A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter ™ and ScienceDirect ™ —was used to identify how non-academics and academics conceptualize and evaluate sentiment indicators associated with the term financial risk in their communications. It was determined that unlike the day-to-day uses of the term—all of which tend to focus predominately on the business and technology aspects of risk taking—the academic definition of the term is expressed broadly. It was also determined that the term was mainly associated with negative emotions in daily conversations, whereas the term tended to be used in a positive way in research paper abstracts. Results from this study suggest that the way financial risk is conceptualized and applied in real-life settings primarily represents negative emotional contexts, while academic papers tend to represent positive emotional contexts. Information presented in this paper can help educators, researchers, and policy makers better understand the way non-academics objectively and subjectively evaluate and describe financial risk. This information may help lead to better investor educational interventions and decision outcomes.
ArticleNumber	6
Author	Grable, John E. Kwak, Eun Jin
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Snippet	A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter ™ and ScienceDirect ™ —was used to... A text mining technique, based on an Application Programming Interface (API) request-using narrative data from Twitter and ScienceDirect -was used to identify... A text mining technique, based on an Application Programming Interface (API) request—using narrative data from Twitter™ and ScienceDirect™—was used to identify... A text mining technique, based on an Application Programming Interface (API) request-using narrative data from Twitter™ and ScienceDirect™-was used to identify...
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SubjectTerms	Abstracts Academic staff Application programming interface Applications of Graph Theory and Complex Networks Computer Science Concept formation Data analysis Data mining Data Mining and Knowledge Discovery Economics Education Emotions Game Theory Humanities Law Library associations Mass murders Medical libraries Methodology of the Social Sciences Negative emotions Original Original Article Perceptions Policy making Researcher subject relations Risk Risk behavior Risk communication Risk taking Scientific papers Social and Behav. Sciences Social networks Statistics for Social Sciences Teachers Verbal communication
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Title	Conceptualizing the use of the term financial risk by non-academics and academics using twitter messages and ScienceDirect paper abstracts
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