Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets

Palicki, Sean-Kelly and Fouad, Shereen and Adedoyin-Olowe, Mariam and Abdallah, Zahraa S. (2021) Transfer Learning Approach for Detecting Psychological Distress in Brexit Tweets. In: The 36th ACM/SIGAPP Symposium On Applied Computing, Machine Learning and its applications track, March 2021, South Korea.

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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 sen- tences describing the depressive or anxiety status. To address this problem, this paper leverages a transfer learning approach for sen- timent 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 do- main) 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.

Item Type: Conference or Workshop Item (Paper)
20 November 2020Accepted
25 January 2021Published Online
Uncontrolled Keywords: Transfer learning, Sentiment analysis, Brexit, Psychological distress,Social media analytics
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Shereen Fouad
Date Deposited: 17 Dec 2020 14:15
Last Modified: 12 Jan 2022 12:58

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