A Meta Learner Strategy for Enhanced Techno stress Detection

The widespread use of technology has led to an increase in technostress which is a phenomenon where individuals experience stress and anxiety due to their interactions with technology. As social media platforms become increasingly integral to daily life, detecting technostress from online interactions has become a pressing concern and an avenue to enrich the research in the area of detecting technostress. This study evaluates the performance of a meta learner strategy using Support Vector Classifier following the implementation of selected base models on X (Twitter data). Also, the study investigated the effectiveness of a feature extraction technique for the improvement of the model performance through data preprocessing including the use of lemmatization and polarity scoring technique. The study made use of the dataset of X posts (Sentiment140) obtained from the Standford University. The extracted features were used to train and evaluate four base models: Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), and Light Gradient Boosting Machine (LGBM). The results of the base models were then used as meta features for the meta learner strategy. The performance of the stacked ensemble shows that the meta learner strategy improved substantially the detection of technostress with improved performance across the evaluation metrics such as accuracy, precision, recall, f1-score, and Kappa score values of 97.03%, 96.88%, 93.92%, 91.63%, and 87.60% respectively. The results highlight the importance stack ensembling in improving model performance; contributes to the development of more effective technostress detection systems and provide insights into the application of machine learning algorithms for analysing online interactions.