[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124936-en":3,"doc-seo-124936-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124936,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Suicidal Tweets Detection in Online Social Media using Machine Learning","The project presents text-content analysis to identify suicidal tendencies and categorize suicide-related types in online social media. It builds a sentence classifier based on a neural network implemented with common machine-learning libraries in Python. The study focuses on teenage suicide risks and the spread of self-harm propaganda through social networks, including the evaluation of “groups of death” information distribution on the internet. It uses collected data from individuals with different levels of suicidal activity and aims to classify sentences as suicidal or non-suicidal via binary classification.","~~ ~~ Research Article  \nSuicidal Tweets Detection in Online social media using Machine Learning  \nNithya Bandari, Mounika Kancharla, Umarani Kunsoth  \nDepartment of Electronics and Communication Engineering  \nSree Dattha Group of Institutions, Hyderabad, Telangana, India.  \nABSTRACT  \nThis project describes content analysis of text with to identify suicidal tendencies and types. This article also describes how to make a sentence classifier that uses a neural network created using various libraries created for machine learning in the Python programming language. Attention is paid to the problem of teenage suicide and «groups of death» in social networks, the search for ways to stop the propaganda of suicide among minors. Analysis of existing information about so-called «groups of death» and its distribution on the Internet.  \nThe study experience of content analysis of suicidal statements on the Internet of persons with different levels of suicidal activity» collects data from the pages of people who have committed suicide or are potential suicides. By analyzing the collected information, program called TextAnalyst explores the causes of suicidal behavior and their feelings. The aim of the current study is to classify sentences into suicidal and non-suicidal using a neural network. In our system, according to random text, it is necessary to determine whether it is suicidal or not, i.e., to solve the problem of its binary classification. Classification is the distribution of data by parameters.  \nKeywords: Suicidal tweets, online social media, machine learning.  \n1. INTRODUCTION  \nAs per the world Health Organization (WHO), suicide is a primary cause of death among individuals between 15-29 years old across the world. 8, 00,000 of people commit suicide every year leading to increase in suicidal ideation. However, an individual person suicide plays an unsocial act that has overwhelming impact towards relations and families. [1] Several suicidal demises are inevitable and very significant to know the behaviour and the way how individual communicate thoughts and depression for inhibiting such deaths. Suicidal avoidance predominantly focuses on monitoring and observation of suicidal efforts and self-harm tendencies. The existence of content related to suicidal ideation plays a major role on the internet for the people seeking for help and offering support through the younger generation. [2] It is observed that, social media data from different blogs and websites (Facebook, Twitter etc.) are used to recognize the affected individuals instantly to offer help. Suicidal behaviour refers to all promising act of self-harm causing death, while suicidal ideation relates to depressive feeling of planning suicide or killing oneself. Although twitter deliver a chance to know the problem of an individual and to provide a potential way for the intervention of both in social level and individual for suicide prevention there exist no better practices using social media [3] . Suicide avoidance by suicidal identification is a best approach to radically reduce suicidal rates. The major experimental application of this work lies in its flexibility to any web-based social network that should be easily adaptable, wherein it tends to be utilized straightforwardly for breaking down textbased tweets posted by its clients and the tweets are flagged if its contents are related to suicidal thoughts. [4] In recent years, many existing studies focused on n-grams, like 3-grams and 5-grams that are used as keywords and phrases as search terms for suicidal prediction. [5] The objective is, to discover opinion, identify sentiment based on tweets posted by the people and classify them for decision making and suicidal prediction by lexicon and machine learning approach and to identify the suicide prediction level of the twitter users based on the twitter dataset by,  \n~~ ~~ Research Article  \n􀁸 Creating the dataset to extract knowledge from the patterns in posted tw","cbCaisRUkm1bO0Cr","https://ap.wps.com/l/cbCaisRUkm1bO0Cr","pdf",574319,1,10,"English","en",105,"# Introduction\n## Problem Statement\n## Motivation\n# Literature Survey","[{\"question\":\"What problem does the project address?\",\"answer\":\"The project addresses detecting suicidal tendencies in online social media text, especially related to teenage suicide and harmful “groups of death” content.\"},{\"question\":\"How does the system detect suicidal content?\",\"answer\":\"It uses a sentence classifier built with a neural network in Python to perform binary classification of sentences into suicidal versus non-suicidal.\"},{\"question\":\"What data sources are used in the study?\",\"answer\":\"Twitter data is collected from different resources using the Twitter API and is used to examine suicidal ideation and related patterns.\"}]","Suicidal Tweets Detection in Online Social Media using Machine Learning | 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