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@article{184869,
        author = {YASARAPU SRUJAN and D.MURALI},
        title = {A SUICIDAL IDEATION DETECTION FRAMEWORK ON SOCIAL MEDIA USING MACHINE LEARNING AND GENETIC ALGORITHMS – BASED FEATURE SELECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3193-3197},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184869},
        abstract = {Suicide is a critical issue worldwide, with early detection of suicidal ideation being essential for prevention. Social media has become a common platform for individuals, especially young people, to express their emotions, including suicidal thoughts, influenced by factors such as depression, anxiety, and social isolation. Detecting such ideation from social media data presents significant challenges in both natural language processing (NLP) and psychology. The dataset used for this analysis, "Suicide_Detection," includes text data from social media posts, with features extracted using TFIDF and NGRAM methods, applied to both original and linguistic features. This study proposes a novel approach to effectively detect suicidal ideation by utilizing a genetic algorithm for feature selection, enhancing model performance. The analysis includes various machine learning algorithms, with the Voting Classifier combining Random Forest, Decision Tree, and XGBoost achieving the highest performance. The model achieved 97.45% accuracy with original genetic features and 95.50% accuracy with linguistic genetic features, demonstrating the effectiveness of the proposed approach in detecting suicidal ideation from social media content.},
        keywords = {Suicide Detection, Suicidal Ideation, Social Media Analysis, Natural Language Processing (NLP), TF–IDF, N-gram, Genetic Algorithm, Feature Selection, Machine Learning, Voting Classifier, Random Forest, Decision Tree, XGBoost},
        month = {September},
        }