  Sentiment analysis has been widely used to understand our views on social and
political agendas or user experiences over a product. It is one of the cores
and well-researched areas in NLP. However, for low-resource languages, like
Bangla, one of the prominent challenge is the lack of resources. Another
important limitation, in the current literature for Bangla, is the absence of
comparable results due to the lack of a well-defined train/test split. In this
study, we explore several publicly available sentiment labeled datasets and
designed classifiers using both classical and deep learning algorithms. In our
study, the classical algorithms include SVM and Random Forest, and deep
learning algorithms include CNN, FastText, and transformer-based models. We
compare these models in terms of model performance and time-resource
complexity. Our finding suggests transformer-based models, which have not been
explored earlier for Bangla, outperform all other models. Furthermore, we
created a weighted list of lexicon content based on the valence score per
class. We then analyzed the content for high significance entries per class, in
the datasets. For reproducibility, we make publicly available data splits and
the ranked lexicon list. The presented results can be used for future studies
as a benchmark.
