Research Publication:
Paper Title: Depression Analysis of Social Media Activists Using the Gated Architecture Bi-LSTM
Authors: Shereen Zehra Rizvi, Momina Rizwan, Amanullah Yasin, Mohsan Ali.
Abstract: In our research, we conducted sentiment analysis on Twitter data to detect depression using advanced deep learning models, including bi-directional LSTM and Convolutional Neural Networks. We designed a webpage connected to the Twitter API for data access. This study is valuable as it offers an efficient method to identify depression among Twitter users by achieving a high accuracy rate of nearly 95% with the bi-directional LSTM model, contributing to mental health awareness and support through social media analysis.
Publication Information: Published by IEEE, in the 2021 International Conference on Cyber Warfare and Security (ICCWS) on the 09 February 2022 in Islamabad.
Link: https://ieeexplore.ieee.org/document/9703014
Highlights: Achieved a high accuracy rate of nearly 95% with the bi-directional LSTM model.
Research Keywords: —twitter data, depression detection, deep-learning, RNN, CNN, Twitter API
Visuals:
Presenting at the 2021 International Conference on Cyber Warfare and Security (ICCWS)
Receiving my certificate from the President of the conference.
Presenting at the 2021 International Conference on Cyber Warfare and Security (ICCWS)