Research Area: Machine Learning
Nowadays, micro-blogging sites like Twitter, Facebook, YouTube, etc., have become much popular for social interactions. People are expressing their depression over social media, which can be analyzed to identify causes behind their depression. Most of the researches on emotion and depression analysis are based on questionnaires and academic interviews in non-Bengali languages, especially English. These traditional methods are not always suitable for detecting human depression. In this paper, we introduced Gated Recurrent Neural Network based depression analysis approach on Bangla social media data. We collected Bangla data from Twitter, Facebook and other sources. We selected four hyper-parameters, namely, number of Gated Recurrent Unit (GRU) layers, layer size, batch size and number of epochs, and presented step by step tuning for these Hyper-parameters. The results show the effects of these tuning steps and how the steps can be beneficial in configuring GRU models for gaining high accuracy on a significantly smaller data set. This will help psychologists and concerned authorities of society detect depression among Bangla speaking social media users. It will also help researchers to implement Natural Language Processing tasks with Deep Learning methods.
Keywords:
Depression Analysis
Bangla Social Media Data
Gated Recurrent Neural Network
Machine Learning
Deep Learning
Author(s) Name: Abdul Hasib Uddin; Durjoy Bapery; Abu Shamim Mohammad Arif
Journal name:
Conferrence name: 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)
Publisher name: IEEE
DOI: 10.1109/ICASERT.2019.8934455
Volume Information:
