  The number of Hindi speakers on social media has increased dramatically in
recent years. Regret is a common emotional experience in our everyday life.
Many speakers on social media, share their regretful experiences and opinions
regularly. It might cause a re-evaluation of one's choices and a desire to make
a different option if given the chance. As a result, knowing the source of
regret is critical for investigating its impact on behavior and
decision-making. This study focuses on regret and how it is expressed,
specifically in Hindi, on various social media platforms. In our study, we
present a novel dataset from three different sources, where each sentence has
been manually classified into one of three classes "Regret by action", "Regret
by inaction", and "No regret". Next, we use this dataset to investigate the
linguistic expressions of regret in Hindi text and also identify the textual
domains that are most frequently associated with regret. Our findings indicate
that individuals on social media platforms frequently express regret for both
past inactions and actions, particularly within the domain of interpersonal
relationships. We use a pre-trained BERT model to generate word embeddings for
the Hindi dataset and also compare deep learning models with conventional
machine learning models in order to demonstrate accuracy. Our results show that
BERT embedding with CNN consistently surpassed other models. This described the
effectiveness of BERT for conveying the context and meaning of words in the
regret domain.
