[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128520-en":3,"doc-seo-128520-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128520,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Enhancing Mental Health Awareness through Twitter Analysis - A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques","This study uses Twitter-derived data—tweets and comments—to gain insights into individuals’ mental health conditions and to support earlier detection and intervention. Depression-related keywords guide data collection, and two methods are evaluated: a Random Forest classifier using TF-IDF and a hybrid CNN-LSTM model using word2vec. The CNN-LSTM approach achieves 89.4% accuracy. A user interface analyzes Twitter profiles and triggers automated reply messages with support resources.","Enhancing Mental Health Awareness through Twitter Analysis: A Comparative Study of Machine Learning and Hybrid Deep Learning Techniques  \nRohini Kancharapu1, Sri Nagesh Ayyagari2  \n1Gayatri Vidya Parishad College of Engineering for Women  \nCSE Department, Kommadhi  \nVisakhapatnam, India  \n[rohinik3108@gmail.com](rohinik3108@gmail.com)  \n2Rayapati Venkata Rangarao & Jagarlamudi Chandramouli College of Engineering,  \nCSE Department, Chowdavaram  \nGuntur, India.  \n[asrinagesh11@gmail.com](asrinagesh11@gmail.com)  \nAbstract—This study explores the utilization of social media data, specifically tweets and comments, for gaining insights into individuals'mental health conditions. The objective is to enhance mental health awareness and enable early detection and intervention. Twitter data is collected using depression-related keywords, and two models are employed: a Random Forest model with TF-IDF and a hybrid CNN-LSTM model incorporating word2vec. The performance of the CNN-LSTM model surpasses that of the Random Forest model, achieving an accuracy rate of 89.4% . Furthermore, a user interface is developed to analyze users' Twitter profiles based on their tweets, allowing for potential intervention through automated reply messages. By harnessing social media data and advanced machine learning techniques, this research contributes to improving mental health awareness and timely addressing of mental health concerns.  \nKeywords-Convolution Neural Network(CNN); Depressed Keywords; Long Short Term Memory (LSTM); Random Forest (RF); Twitter; VADER.  \nI. INTRODUCTION  \nDepression is a pervasive mental health disorder that affects a significant portion of the global population, leading to substantial personal, social, and economic burdens. According to the World Health Organization (WHO), depression is ranked as the leading cause of disability worldwide [21,24] . Timely detection and intervention are crucial in addressing this public health issue effectively [26] . With advancements in technology, mental health professionalsand researchers now have a powerful tool at their disposal – social media data. By monitoring and analyzing negative emotions and attitudes expressed on these platforms, we can identify individuals at risk and provide them with the necessary support and resources.  \nIn recent years, the advent of social media platforms has revolutionized communication and provided individuals with a digital space to express their thoughts, emotions, and experiences [5-7] . Social media platforms, such as Twitter, have become a vast repository of user-generated content, offering valuable insights into people's lives, including their mental well-being [1] . Recognizing the potential of social media data for mental health research, scholars have explored  \nnovel approaches to analyze and leverage this data effectively. One promising avenue is sentiment analysis [25], a computational technique that focuses on understanding and extracting emotional signals from text data.  \nThis study aims to harness the power of sentiment analysis and social media data to enhance mental health awareness and early detection of depression. Specifically, we focus on Twitter as a source of data, given its popularity and extensive user base. By extracting data from Twitter using specific keywords associated with depression, we delve into the vast pool of information available on the platform. By analyzing tweets and comments, we can uncover valuable information regarding individuals' mental states and emotional well-being.  \nTo achieve this, we employ advanced machine learning techniques to process and analyze the vast amount of textual data available on Twitter. In our analysis of Twitter data, we utilize the VADER algorithm to determine the polarity score of tweets and categorize users' mental health status based on their content [21] . Additionally, we employ text-to-vector conversion techniques such as Word2vec and TF-IDF, ), enable us to transform text into n","cbCaimsMkvib0dmE","https://ap.wps.com/l/cbCaimsMkvib0dmE","pdf",608654,2,1,13,"English","en",105,"# Introduction\n## Depression and the Need for Timely Detection\n## Social Media as a Data Source\n## Sentiment Analysis and Computational Signals\n## Study Approach and Models\n## Practical Interface and Intervention Workflow","[{\"question\":\"How is Twitter data collected in this study?\",\"answer\":\"Tweets and comments are collected using depression-related keywords, then processed to support mental health assessment through text analysis.\"},{\"question\":\"What machine learning models are compared?\",\"answer\":\"The study compares a Random Forest model using TF-IDF with a hybrid CNN-LSTM model that incorporates word2vec representations.\"},{\"question\":\"What performance result does the hybrid CNN-LSTM model achieve?\",\"answer\":\"The CNN-LSTM model outperforms Random Forest and reaches an accuracy of 89.4%.\"}]","Enhancing Mental Health Awareness through Twitter Analysis - 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