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Title |
Detection of Depression Trends in Literary Cyber Writers Using Sentiment Analysis and Machine Learning |
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Author |
Faiza Nasir, Haseeb Ahmad, CM Nadeem Faisal, Qaisar Abbas, Mubarak Albathan, Ayyaz Hussain |
| Citation |
Vol. 23 No. 3 pp. 67-80 |
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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 de-pression 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: |
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Keywords |
Depression detection; SentiStrength; TF-IDF; N-Gram; Classification; Machine Learning; Sentiment Analysis |
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