  In recent years, several systems have been developed to regulate the spread
of negativity and eliminate aggressive, offensive or abusive contents from the
online platforms. Nevertheless, a limited number of researches carried out to
identify positive, encouraging and supportive contents. In this work, our goal
is to identify whether a social media post/comment contains hope speech or not.
We propose three distinct models to identify hope speech in English, Tamil and
Malayalam language to serve this purpose. To attain this goal, we employed
various machine learning (support vector machine, logistic regression,
ensemble), deep learning (convolutional neural network + long short term
memory) and transformer (m-BERT, Indic-BERT, XLNet, XLM-Roberta) based methods.
Results indicate that XLM-Roberta outdoes all other techniques by gaining a
weighted $f_1$-score of $0.93$, $0.60$ and $0.85$ respectively for English,
Tamil and Malayalam language. Our team has achieved $1^{st}$, $2^{nd}$ and
$1^{st}$ rank in these three tasks respectively.
