An Attention-Based Mood Controlling Framework for Social Media Users

Tapotosh Ghosh, Md. Hasan Al Banna, Tazkia Mim Angona, M. Jaber Al Nahian, Mohammed Nasir Uddin, M. Shamim Kaiser, Mufti Mahmud

Abstract: In this digital age, social media is an essential part of life. People share their moments and emotions through it. Consequently, detecting emotions in their behavior can be an effective way to determine their emotional disposition, which can then be used to control their negative thinking by making them see the positive aspects of the world. This study proposes an emotion detection-based mood control framework that reorganizes social media posts to match the user’s mental state. An emotion detection model based on Attention mechanism, Bidirectional Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN) has been proposed which can detect six emotions from Bangla text with 66.98% accuracy. It also demonstrates how emotion detection frameworks can be implemented in other languages as well.