Matches in SemOpenAlex for { <https://semopenalex.org/work/W4328010982> ?p ?o ?g. }
- W4328010982 abstract "Depression is a mental illness that affects a person’s feelings and causes them to be negative. According to the WHO (World Health Organization), 280 million people suffer from depression, and it is a main cause of suicide and carries a great burden of disease. Given that social media is the number one source nowadays for expressing a person’s emotions and feelings, it provides a proper environment to harvest raw data and detect signs of depression by analyzing the content shared by users. In this paper, we proposed a model for the detection of depression using Twitter as a source of information as it is one of the most popular social media platforms. Moreover, we found that research on such topics is lacking for the Arabic language and Arab users even though Arabic is the 4th most used language on Twitter. Therefore, we aimed to detect depression by identifying depressive tweets using natural language processing (NLP) tools and techniques to reveal the sentiments expressed by Arabs in their tweets and the polarity of depression. We have applied machine learning algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Naïve Bayes (NB) on tweets that are of the Saudi Dialect of the Modern Arabic Language to identify depressive tweets. Along with a combined Term Frequency - Inverse Document Frequency (TFIDF) and N-gram feature extraction approach, we concluded that combining TF-IDF with N-gram produces better results. We also found that Logistic Regression outperformed the other algorithms with an accuracy of 82%." @default.
- W4328010982 created "2023-03-22" @default.
- W4328010982 creator A5026955621 @default.
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- W4328010982 date "2022-12-17" @default.
- W4328010982 modified "2023-10-16" @default.
- W4328010982 title "Depression Detection Through Identifying Depressive Arabic Tweets From Saudi Arabia: Machine Learning Approach" @default.
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- W4328010982 doi "https://doi.org/10.1109/nccc57165.2022.10067346" @default.
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