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Analyzing hope speech from psycholinguistic and emotional perspectives.
Arif, Muhammad; Shahiki Tash, Moein; Jamshidi, Ainaz; Ullah, Fida; Ameer, Iqra; Kalita, Jugal; Gelbukh, Alexander; Balouchzahi, Fazlourrahman.
Afiliação
  • Arif M; Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
  • Shahiki Tash M; Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
  • Jamshidi A; Department of Information Systems, University of Maryland Baltimore County (UMBC), Baltimore, USA.
  • Ullah F; Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
  • Ameer I; Division of Engineering and Science at Abington, The Pennsylvania State University, University Park, USA.
  • Kalita J; University of Colorado, Colorado Springs, USA.
  • Gelbukh A; Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico.
  • Balouchzahi F; Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico. fbalouchzahi2021@cic.ipn.mx.
Sci Rep ; 14(1): 23548, 2024 10 09.
Article em En | MEDLINE | ID: mdl-39384851
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 investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models' parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Psicolinguística / Fala / Processamento de Linguagem Natural / Emoções / Mídias Sociais Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Psicolinguística / Fala / Processamento de Linguagem Natural / Emoções / Mídias Sociais Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2024 Tipo de documento: Article País de afiliação: México País de publicação: Reino Unido