  This article aims to exploit social exchanges on scientific literature,
specifically tweets, to analyse social media users' sentiments towards
publications within a research field. First, we employ the SentiStrength tool,
extended with newly created lexicon terms, to classify the sentiments of
6,482,260 tweets associated with 1,083,535 publications provided by
Altmetric.com. Then, we propose harmonic means-based statistical measures to
generate a specialized lexicon, using positive and negative sentiment scores
and frequency metrics. Next, we adopt a novel article-level summarization
approach to domain-level sentiment analysis to gauge the opinion of social
media users on Twitter about the scientific literature. Last, we propose and
employ an aspect-based analytical approach to mine users' expressions relating
to various aspects of the article, such as tweets on its title, abstract,
methodology, conclusion, or results section. We show that research communities
exhibit dissimilar sentiments towards their respective fields. The analysis of
the field-wise distribution of article aspects shows that in Medicine,
Economics, Business & Decision Sciences, tweet aspects are focused on the
results section. In contrast, Physics & Astronomy, Materials Sciences, and
Computer Science these aspects are focused on the methodology section. Overall,
the study helps us to understand the sentiments of online social exchanges of
the scientific community on scientific literature. Specifically, such a
fine-grained analysis may help research communities in improving their social
media exchanges about the scientific articles to disseminate their scientific
findings effectively and to further increase their societal impact.
