Identification of Psychological Resilience over Social Media Using Stacked Ensemble Learning Algorithm
Keywords:
Psychological Resilience, Digital Footprint, Stacked Ensemble Learning, Word Embedding, Neurological Disorder, Sentiment AnalysisAbstract
Online communication over social media affecting psychological resilience that helps to pre-identify neurological disorder activities. Currently, existing research commonly uses a single model for such detection. This study suggests a stacked ensemble learning algorithm that ensembles four base classifiers including Support Vector, Random Forest, K Nearest Neighbor, Catboost, and a Meta classifier as Logistic Regression, along with a variety of word embeddings including Word2Vec, GloVe and FastText on the training corpus that is performed over Twitter public dataset to identify such neurological disorder problems among individuals. The training and testing models are tuned and then calculates the efficiency of proposed model in terms of metric calculation scores via Precision, Accuracy, Recall and F1-scores. The proposed ensemble model performed better over standalone models and results are then evaluated using confusion matrix & RoC curves. It also gives comparison based on execution time among all the classifiers. Hence, this research aimed to the earlier disclosure of such symptoms that can helps to increase psychological resilience and ultimately lowering the affect of mental hazard problems.
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