Published December 10, 2022 | Version v1
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COVID-19 Impact on UK Unemployment Rate: A Social Media Sentiment Analysis

  • 1. MSc Artificial Intelligence and Data Science, University of Hull, UK


The COVID-19 pandemic has left its mark across every facet of our life today. Its consequences on unemployment due to restrictions on social interaction among people, economic collapse, and fear of business continuity led people to express their concerns on social media. The Twitter platform had been a source of unstructured data for different COVID-19 analysis. In this work, we have analysed 197,669 tweets by country and cities to utilise sentiment analysis. A natural language processing (NLP) technique for opinion mining to extract neutral, positive and negative sentiments on COVID-19; and its impact on the unemployment rate in the United Kingdom. We investigated deep learning techniques with Long-Short Term Memory networks (LSTMs) and Bidirectional Long-Short Term Memory networks (Bi-LSTM), on Twitter data during the pandemic lockdown, March 2020 and September 2021 after the furlough is closed. Using Bi-LSTM, which gives 91% accuracy and 87% F1-score, precision and recall each. Our study shows that the lockdown witnessed a positive sentiment between March – May 2020 and greater number of negative tweets in the United Kingdom during the peak unemployment rate of June – October, 2020. England and Scotland had similar trends, together with their largest cities London and Glasgow respectively. Furthermore, we observed a significant reduction in negative sentiments tweet responses from 3.67% in July 2020 to 0.83% in May 2021; while the unemployment rate is at its peak. This is attributed to the period the second phase of Coronavirus Job Retention Scheme (CJRS) known as flexible furlough was introduced.

Keywords: COVID-19, UK unemployment rate and social media.



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  • Pennington J, Socher R, Manning CD (2014). Glove: Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).
  • Raza H, Faizan M, Hamza A, Mushtaq A, Akhtar N (2019). Scientific text sentiment analysis using machine learning techniques. Int. J. Adv. Comp. Sci. Appl. 10 (12): 157-165.
  • Rustam F, Khalid M, Aslam W, Rupapara V, Mehmood A, Choi GS (2021a). A performance comparison of supervised machine learning models for covid-19 tweets sentiment analysis. PloS One, 16 (2): e0245909.
  • Rustam F, Khalid M, Aslam W, Rupapara V, Mehmood A, Choi GS (2021b). A performance comparison of supervised machine learning models for covid-19 tweets sentiment analysis. Plos One, 16 (2): e0245909.
  • Hochreiter S, Schmidhuber J (1997). Long short-term memory.
  • Schuster M, Paliwal KK (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45 (11): 2673-2681.
  • Staudemeyer RC, Morris ER (2019). Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv Preprint arXiv:1909.09586.
  • Steven Loria (2020). TextBlob [Blog] TextBlob: Simplified Text Processing. Steven's Blog. 26 Apr. Available Online: [Accessed: 18/05/2022]
  • Van Den Broeck J, Cunningham SA, Eeckels R, Herbst K (2005). Data cleaning: Detecting, diagnosing, and editing data abnormalities. PLoS Medicine. 2 (10): 966.
  • Xu G, Meng Y, Qiu X, Yu Z, Wu X (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7 51522-51532.
  • Yadav A, Vishwakarma DK (2020). Sentiment analysis using deep learning architectures: A review. The Artificial Intelligence Review. 53 (6): 4335-4385.
  • Yue L, Chen W, Li X, Zuo W, Yin M (2019). A survey of sentiment analysis in social media. Knowledge and Information Systems, 60 (2): 617-663.
  • Zhang S, Zheng D, Hu X, Yang M (2015). Bidirectional long short-term memory networks for relation classification. Proceedings of the 29th Pacific Asia conference on language, information and computation.
  • Zhang Aston, Lipton Zachary C, Li Mu, Smola Alexander J (2021). Dive Into Deep Learning. zhang2021dive. Online Source: Accessed : 25/05/2022]