Depressed People Detection from Bangla Social Media Status using LSTM and CNN Approach
DOI:
https://doi.org/10.38032/jea.2021.01.006Keywords:
Depression, CNN-LSTM, SVM, Word Embedding, Neural NetworkAbstract
At present, depression is the main reason for suicidal death. Depression also causes different kinds of diseases. Nowadays, people are deeply involved in social media and like to share their feelings on social media. So, it becomes easy to analyze depression through social media. In this paper, a combination of two CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models has been proposed to make a hybrid CNN-LSTM model, CNN has performed for the image to create a matrix, and LSTM has given the result from the given matrix. In this paper, datasets are prepared based on depression and non-depression-related status. The proposed method has been applied to that dataset. The best result has been obtained using a hybrid neural network with the word embedding technique using the Bengali Facebook status dataset. We have used the SVM (Support Vector Machine) model to predict a small dataset of Bengali Facebook status and count vectorizer to count the word in the document. Finally, this paper has built up a model that makes strength and support for deep learning architecture.
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