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Studying social media sentiment using human validated analysis
The measurement of online sentiment is a developing field in social science and big data research. The methodology from this study provides an analysis of online sentiment using a unique combination of NLP and human validation techniques in order to create net sentiment scores and categorise topics...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152698/ https://www.ncbi.nlm.nih.gov/pubmed/32300546 http://dx.doi.org/10.1016/j.mex.2020.100867 |
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author | Lappeman, James Clark, Robyn Evans, Jordan Sierra-Rubia, Lara Gordon, Patrick |
author_facet | Lappeman, James Clark, Robyn Evans, Jordan Sierra-Rubia, Lara Gordon, Patrick |
author_sort | Lappeman, James |
collection | PubMed |
description | The measurement of online sentiment is a developing field in social science and big data research. The methodology from this study provides an analysis of online sentiment using a unique combination of NLP and human validation techniques in order to create net sentiment scores and categorise topics of online conversation. The study focused on measuring the online sentiment of South Africa's major banks (covering almost the entire retail banking industry) over a 12-month period. Through this methodology, firms are able to track shifts in online sentiment (including extreme firestorms) as well as to monitor relevant conversation topics. To date, no published methodology combines the use of big data NLP and human validation in such a structured way. • Microsampling for manual validation of sentiment analysis (both qualitative and quantitative approaches in order to obtain the most accurate results); • Sentiment measurement; • Sentiment map. |
format | Online Article Text |
id | pubmed-7152698 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-71526982020-04-16 Studying social media sentiment using human validated analysis Lappeman, James Clark, Robyn Evans, Jordan Sierra-Rubia, Lara Gordon, Patrick MethodsX Social Science The measurement of online sentiment is a developing field in social science and big data research. The methodology from this study provides an analysis of online sentiment using a unique combination of NLP and human validation techniques in order to create net sentiment scores and categorise topics of online conversation. The study focused on measuring the online sentiment of South Africa's major banks (covering almost the entire retail banking industry) over a 12-month period. Through this methodology, firms are able to track shifts in online sentiment (including extreme firestorms) as well as to monitor relevant conversation topics. To date, no published methodology combines the use of big data NLP and human validation in such a structured way. • Microsampling for manual validation of sentiment analysis (both qualitative and quantitative approaches in order to obtain the most accurate results); • Sentiment measurement; • Sentiment map. Elsevier 2020-03-19 /pmc/articles/PMC7152698/ /pubmed/32300546 http://dx.doi.org/10.1016/j.mex.2020.100867 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Social Science Lappeman, James Clark, Robyn Evans, Jordan Sierra-Rubia, Lara Gordon, Patrick Studying social media sentiment using human validated analysis |
title | Studying social media sentiment using human validated analysis |
title_full | Studying social media sentiment using human validated analysis |
title_fullStr | Studying social media sentiment using human validated analysis |
title_full_unstemmed | Studying social media sentiment using human validated analysis |
title_short | Studying social media sentiment using human validated analysis |
title_sort | studying social media sentiment using human validated analysis |
topic | Social Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152698/ https://www.ncbi.nlm.nih.gov/pubmed/32300546 http://dx.doi.org/10.1016/j.mex.2020.100867 |
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