Hope Speech are positive terms that help to promote or criticise a point of view without hurting the user’s or community’s feelings. Non-Hope Speech, on the other side, includes expressions that are harsh, ridiculing, or demotivating. The goal of this article is to find the hope speech comments in a YouTube dataset. The datasets were created as part of the “LT-EDI-ACL 2022: Hope Speech Detection for Equality, Diversity, and Inclusion” shared task. The shared task dataset was proposed in Malayalam, Tamil, English, Spanish, and Kannada languages. In this paper, we worked at English-language YouTube comments. We employed several deep learning based models such as DNN (dense or fully connected neural network), CNN (Convolutional Neural Network), Bi-LSTM (Bidirectional Long Short Term Memory Network), and GRU(Gated Recurrent Unit) to identify the hopeful comments. We also used Stacked LSTM-CNN and Stacked LSTM-LSTM network to train the model. The best macro average F1-score 0.67 for development dataset was obtained using the DNN model. The macro average F1-score of 0.67 was achieved for the classification done on the test data as well.