Exploring Multidimensional Aspects of Hope Speech Computationally: A Psycholinguistic and Emotional Perspective

Abstract Hope is a vital coping mechanism, enabling individuals to effectively confront life's challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study explores the potential of using this data to classify various hope types via machine learning algorithms. Notably, models such as LightGBM and CatBoost exhibit promising performance, outperforming traditional methods and rivaling deep learning techniques.