  In 2016, United Kingdom (UK) citizens voted to leave the European Union (EU),
which was officially implemented in 2020. During this period, UK residents
experienced a great deal of uncertainty around the UK's continued relationship
with the EU. Many people have used social media platforms to express their
emotions about this critical event. Sentiment analysis has been recently
considered as an important tool for detecting mental well-being in Twitter
contents. However, detecting the psychological distress status in
political-related tweets is a challenging task due to the lack of explicit
sentences describing the depressive or anxiety status. To address this problem,
this paper leverages a transfer learning approach for sentiment analysis to
measure the non-clinical psychological distress status in Brexit tweets. The
framework transfers the knowledge learnt from self-reported psychological
distress tweets (source domain) to detect the distress status in Brexit tweets
(target domain). The framework applies a domain adaptation technique to
decrease the impact of negative transfer between source and target domains. The
paper also introduces a Brexit distress index that can be used to detect levels
of psychological distress of individuals in Brexit tweets. We design an
experiment that includes data from both domains. The proposed model is able to
detect the non-clinical psychological distress status in Brexit tweets with an
accuracy of 66% and 62% on the source and target domains, respectively.
