  Aggressive comments on social media negatively impact human life. Such
offensive contents are responsible for depression and suicidal-related
activities. Since online social networking is increasing day by day, the hate
content is also increasing. Several investigations have been done on the domain
of cyberbullying, cyberaggression, hate speech, etc. The majority of the
inquiry has been done in the English language. Some languages (Hindi and
Bangla) still lack proper investigations due to the lack of a dataset. This
paper particularly worked on the Hindi, Bangla, and English datasets to detect
aggressive comments and have shown a novel way of generating machine-translated
data to resolve data unavailability issues. A fully machine-translated English
dataset has been analyzed with the models such as the Long Short term memory
model (LSTM), Bidirectional Long-short term memory model (BiLSTM),
LSTM-Autoencoder, word2vec, Bidirectional Encoder Representations from
Transformers (BERT), and generative pre-trained transformer (GPT-2) to make an
observation on how the models perform on a machine-translated noisy dataset. We
have compared the performance of using the noisy data with two more datasets
such as raw data, which does not contain any noises, and semi-noisy data, which
contains a certain amount of noisy data. We have classified both the raw and
semi-noisy data using the aforementioned models. To evaluate the performance of
the models, we have used evaluation metrics such as F1-score,accuracy,
precision, and recall. We have achieved the highest accuracy on raw data using
the gpt2 model, semi-noisy data using the BERT model, and fully
machine-translated data using the BERT model. Since many languages do not have
proper data availability, our approach will help researchers create
machine-translated datasets for several analysis purposes.
