Predicting literature's early impact with sentiment analysis in Twitter
Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Authors
Hassan, SUAljohani, Naif Radi
Idrees, N
Sarwar, R
Nawaz, R
Martínez-Cámara, E
Ventura, S
Herrera, F
Issue Date
2019-12-14
Metadata
Show full item recordAbstract
© 2019 Elsevier B.V. Traditional bibliometric techniques gauge the impact of research through quantitative indices based on the citations data. However, due to the lag time involved in the citation-based indices, it may take years to comprehend the full impact of an article. This paper seeks to measure the early impact of research articles through the sentiments expressed in tweets about them. We claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. We used the SentiStrength tool and improved it by incorporating new opinion-bearing words into its sentiment lexicon pertaining to scientific domains. Then, we classified the sentiment of 6,482,260 tweets linked to 1,083,535 publications covered by Altmetric.com. Using positive and negative tweets as an independent variable, and the citation count as the dependent variable, linear regression analysis showed a weak positive prediction of high citation counts across 16 broad disciplines in Scopus. Introducing an additional indicator to the regression model, i.e. ‘number of unique Twitter users’, improved the adjusted R-squared value of regression analysis in several disciplines. Overall, an encouraging positive correlation between tweet sentiments and citation counts showed that Twitter-based opinion may be exploited as a complementary predictor of literature's early impact.Citation
Hassa, S.U., Aljohani, N.R., Idrees, N., Sarwar, R., Nawaz, R., Martínez-Cámara, E., Ventura, S. and Herrera, F. (2020) Predicting literature's early impact with sentiment analysis in Twitter, Knowledge-Based Systems, 192, 105383.Publisher
ElsevierJournal
Knowledge-Based SystemsType
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by Elsevier in Knowledge-Based Systems on 14/12/2019, available online: https://doi.org/10.1016/j.knosys.2019.105383 The accepted version of the publication may differ from the final published version.ISSN
0950-7051ae974a485f413a2113503eed53cd6c53
10.1016/j.knosys.2019.105383
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/