Browsing by Author "Dehkharghani, Rahim"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 14
    Citation - Scopus: 20
    Mental Disorder and Suicidal Ideation Detection From Social Media Using Deep Neural Networks
    (Springernature, 2024) Ezerceli, Ozay; Dehkharghani, Rahim
    Depression and suicidal ideation are global reasons for life-threatening injury and death. Mental disorders have increased especially among young people in recent years, and early detection of those cases can prevent suicide attempts. Social media platforms provide users with an anonymous space to interact with others, making them a secure environment to discuss their mental disorders. This paper proposes a solution to detect depression/suicidal ideation using natural language processing and deep learning techniques. We used Transformers and a unique model to train the proposed model and applied it to three different datasets: SuicideDetection, CEASEv2.0, and SWMH. The proposed model is evaluated using the accuracy, precision, recall, and ROC curve. The proposed model outperforms the state-of-the-art in the SuicideDetection and CEASEv2.0 datasets, achieving F1 scores of 0.97 and 0.75, respectively. However, in the SWMH data set, the proposed model is 4% points behind the state-of-the-art precision providing the F1 score of 0.68. In the real world, this project could help psychologists in the early detection of depression and suicidal ideation for a more efficient treatment. The proposed model achieves state-of-the-art performance in two of the three datasets, so they could be used to develop a screening tool that could be used by mental health professionals or individuals to assess their own risk of suicide. This could lead to early intervention and treatment, which could save lives.
  • Thumbnail Image
    Article
    Sarcasm Detection on News Headlines Using Transformers
    (Springer, 2025) Gumuscekicci, Gizem; Dehkharghani, Rahim
    Sarcasm poses a linguistic challenge due to its figurative nature, where intended meaning contradicts literal interpretation. Sarcasm is prevalent in human communication, affecting interactions in literature, social media, news, e-commerce, etc. Identifying the true intent behind sarcasm is challenging but essential for applications in sentiment analysis. Detecting sarcasm in written text, as a challenging task, has attracted many researchers in recent years. This paper attempts to detect sarcasm in news headlines. Journalists prefer using sarcastic news headlines as they seem much more interesting to the readers. In the proposed methodology, we experimented with Transformers, namely the BERT model, and several Machine and Deep Learning models with different word and sentence embedding methods. The proposed approach inherently requires high-performance resources due to the use of large-scale pre-trained language models such as BERT. We also extended an existing news headlines dataset for sarcasm detection using augmentation techniques and annotating it with hand-crafted features. The proposed methodology could outperform almost all existing sarcasm detection approaches with a 98.86% F1-score when applied to the extended news headlines dataset, which we made publicly available on GitHub.
  • Thumbnail Image
    Master Thesis