Investigating mental wellbeing in the construction industry using social media sentiment analysis: a UK case study

Abstract

Mental health challenges represent a significant global concern, with depression affecting approximately 4% of the population. In the United Kingdom, approximately one in four individuals will experience a mental health problem, with the construction industry particularly vulnerable due to its demanding work environment. This investigation employs a historical dataset (May 2008 to March 2016) to establish the methodological feasibility of social media sentiment analysis for detecting occupational stress indicators in construction-related discourse. Rather than claiming contemporary generalizability, this work demonstrates proof-of-concept that automated sentiment analysis can reliably detect emotional patterns from social media data. By leveraging user-generated content from Twitter, 2,322 tweets were extracted and subjected to exploratory analysis, sentiment analysis, topic modelling analysis, and machine learning classification. Emotion labels were assigned using NRCLex lexicon-based methods and validated against human ratings (κ = 0.79). Machine learning classifiers were applied to categorise tweets into different emotions using n-grams as input features. Results indicated that uni-gram features offered superior performance across all classifiers compared to bi-gram and tri-gram feature sets. When trained with default hyperparameters, traditional machine learning methods such as logistic regression and support vector classifier achieved comparable performance to neural network methods in detecting anticipation emotion (AUC 0.93 vs 0.90), with similar patterns observed for other emotion categories. Topic modelling identified ten substantive themes in construction-related discourse, with employment security concerns embedded throughout technical discussions. The findings establish methodological proof-of-concept for real-time sentiment monitoring in occupational settings, with recommendations for contemporary replication to assess current construction industry emotional patterns.