Our research in Natural Language Processing (NLP) aims to detect hate speech comments specifically targeted at the LGBTQ+ community within the YouTube platform shared task conducted by LTEDI workshop. The dataset provided by the organizers exhibited a high degree of class imbalance, and to mitigate this, we employed NLPAUG, a data augmentation library. We employed several classification methods and reported the results using recall, precision, and F1-score metrics. The classification models discussed in this paper include a Bidirectional Long Short-Term Memory (BiLSTM) model trained with Word2Vec embeddings, a BiLSTM model trained with Twitter GloVe embeddings, transformer models such as BERT, DistiBERT, RoBERTa, and XLM-RoBERTa, all of which were trained and fine-tuned. We achieved a weighted F1-score of 0.699 on the test data and secured fifth place in task B with 7 classes for the English language.