International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Detection of Depression Trends in Literary Cyber Writers Using Sentiment Analysis and Machine Learning

  • Faiza Nasir (Department of Computer Science, University of Engineering and Technology) ;
  • Haseeb Ahmad (Department of Computer Science, University of Engineering and Technology) ;
  • CM Nadeem Faisal (Department of Computer Science, University of Engineering and Technology) ;
  • Qaisar Abbas (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Mubarak Albathan (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)) ;
  • Ayyaz Hussain (Department of Computer Science, Quaid-i-Azam University)
  • Received : 2023.03.05
  • Published : 2023.03.30
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Abstract

Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.

Keywords

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this work through Research Partnership Program no. RP-21-07-11.

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