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Sentiment Analysis (SA) is an action research area in the digital age. Withrapid and constant growth of online social media sites and services, and theincreasing amount of textual data such as - statuses, comments, reviews etc.available in them, application of automatic SA is on the rise. However, most ofthe research works on SA in natural language processing (NLP) are based onEnglish language. Despite being the sixth most widely spoken language in theworld, Bangla still does not have a large and standard dataset. Because ofthis, recent research works in Bangla have failed to produce results that canbe both comparable to works done by others and reusable as stepping stones forfuture researchers to progress in this field. Therefore, we first tried toprovide a textual dataset - that includes not just Bangla, but Romanized Banglatexts as well, is substantial, post-processed and multiple validated, ready tobe used in SA experiments. We tested this dataset in Deep Recurrent model,specifically, Long Short Term Memory (LSTM), using two types of loss functions- binary crossentropy and categorical crossentropy, and also did someexperimental pre-training by using data from one validation to pre-train theother and vice versa. Lastly, we documented the results along with someanalysis on them, which were promising.

Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models