ABSTRAT:
Suicide is a critical issue in modern society. Early detection and prevention of suicide attempts should be addressed to save people’s life. Current suicidal ideation detection (SID) methods include clinical methods based on the interaction between social workers or experts and the targeted individuals and machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents. This article is the first survey that comprehensively introduces and discusses the methods from these categories. Domain-specific applications of SID are reviewed according to their data sources, i.e., questionnaires, electronic health records, suicide notes, and online user content. Several specific tasks and data sets are introduced and summarized to facilitate further research. Finally, we summarize the limitations of current work and provide an outlook of further research directions.
EXISTING SYSTEM :
Traditional suicide detection relies on clinical meth- ods, including self-reports and face-to-face interviews. Venek et al. [9] designed a five-item ubiquitous questionnaire for the assessment of suicidal risks and applied a hierarchical classifier on the patients’ response to determine their suici- dal intentions. Through face-to-face interaction, verbal and acoustic information can be utilized. Scherer [23] investigated the prosodic speech characteristics and voice quality in a dyadic interview to identify suicidal and nonsuicidal juveniles. Other clinical methods examine the resting state heart rate from converted sensing signals [24] and classify the functional magnetic resonance imaging-based neural representations of death- and life-related words [25] and event-related instigators converted from EEG signals [26]. Another aspect of clinical treatment is the understanding of the psychology behind suicidal behavior [5], which, however, relies heavily on the clinician’s knowledge and face-to-face interaction. Suicide risk assessment scales with clinical interview can reveal informa- tive cues for predicting suicide [27]. Tan et al. [28] conducted an interview and survey study in Weibo, a Twitter-like service in China, to explore the engagement of suicide attempters with intervention by direct messages.
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
he popularization of machine learning has facilitated research on SID from multimodal data and provided a promis- ing way for effective early warning. Current research focuses on text-based methods by extracting features and deep learning for automatic feature learning. Researchers widely use many canonical NLP features, such as TF-IDF, topics, syntactic, affective characteristics, readability, and deep learning mod- els, such as CNN and LSTM. Those methods, especially DNNs with automatic feature learning, boosted predictive performance and preliminary success on suicidal intention understanding. However, some methods may only learn sta- tistical cues and lack of commonsense. The recent work [58] incorporated external knowledge using knowledge bases and suicide ontology for knowledge-aware suicide risk assessment. It took a remarkable step toward knowledge-aware detection
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
