  While new technologies are expected to revolutionise and become game-changers
in improving the efficiencies and practises of our daily lives, it is also
critical to investigate and understand the barriers and opportunities faced by
their adopters. Such findings can serve as an additional feature in the
decision-making process when analysing the risks, costs, and benefits of
adopting an emerging technology in a particular setting. Although several
studies have attempted to perform such investigations, these approaches adopt a
qualitative data collection methodology which is limited in terms of the size
of the targeted participant group and is associated with a significant manual
overhead when transcribing and inferring results. This paper presents a
scalable and automated framework for tracking likely adoption and/or rejection
of new technologies from a large landscape of adopters. In particular, a large
corpus of social media texts containing references to emerging technologies was
compiled. Text mining techniques were applied to extract sentiments expressed
towards technology aspects. In the context of the problem definition herein, we
hypothesise that the expression of positive sentiment infers an increase in the
likelihood of impacting a technology user's acceptance to adopt, integrate,
and/or use the technology, and negative sentiment infers an increase in the
likelihood of impacting the rejection of emerging technologies by adopters. To
quantitatively test our hypothesis, a ground truth analysis was performed to
validate that the sentiment captured by the text mining approach is comparable
to the results given by human annotators when asked to label whether such texts
positively or negatively impact their outlook towards adopting an emerging
technology.
