Depression detection from Social Media Bangla Text Using Recurrent Neural Networks

TL;DR

This paper explores depression detection from Bangla social media texts using natural language processing and recurrent neural networks, aiming to assist mental health diagnosis through sentiment analysis.

Contribution

It introduces a novel approach applying RNNs and machine learning algorithms to detect depression in Bangla social media posts, with detailed preprocessing and feature extraction techniques.

Findings

LSTM achieved the highest accuracy among classifiers

Preprocessing improved model performance

The approach can assist psychologists in early depression detection

Abstract

Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment.