Abstract
Recently altmetrics (short for alternative metrics) are gaining popularity among researchers to identify the impact of scholarly publications among the general public. Although altmetrics have been widely used nowadays, there has been a limited number of studies analyzing users’ sentiments towards these scholarly publications on social media platforms. In this paper, we analyzed and compared user sentiments (positive, negative and neutral) towards scholarly publications in Medicine and Psychiatry domains by analyzing user-generated content (tweets) on Twitter. We explored various machine learning algorithms, and constructed the best model with Support Vector Machine (SVM) which gave an accuracy of 91.6%.
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References
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Bharathwaj, S.K., Na, JC., Sangeetha, B., Sarathkumar, E. (2019). Sentiment Analysis of Tweets Mentioning Research Articles in Medicine and Psychiatry Disciplines. In: Jatowt, A., Maeda, A., Syn, S. (eds) Digital Libraries at the Crossroads of Digital Information for the Future. ICADL 2019. Lecture Notes in Computer Science(), vol 11853. Springer, Cham. https://doi.org/10.1007/978-3-030-34058-2_29
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DOI: https://doi.org/10.1007/978-3-030-34058-2_29
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