An Analogy of CNN and LSTM Model for Depression Detection with Multiple Epoch

Depression is a mental disorder which with mild effect may cause the feeling of sadness feeling and at worst effect may lead to sucicide. Beside these effects of depression are least talked diseases due to social stigma, unawareness about it. Twitter is a social media platform for self-disclosure of the feeling, people express their happiness and sadness on the platform. So twitter is a good source of sentimental text.Foregoing research for the classification of tweet data for the depressive and non depressive class is performed with SVM,RNN GRU, and CNN. The present paper aims to analyse the behaviour of different deep learning models as a convolutional neural network with global max pooling, convolutional neural network with global average pooling, for the classification of tweet data as depressive and non depressive classes at different epochs 5, 10, 15. The Result concludes that the LSTM model achieves the accuracy of 99.19% followed by LSTM with CNN with 99.16%.