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

arXiv: https://arxiv.org/abs/1610.00369 · HTML: https://mlsanity.com/paper/1610.00369/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2016.

## Built on by (most cited first, 7 of 7)

- [BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews](https://mlsanity.com/paper/2305.06595/) (2023)
- [A Review of Bangla Natural Language Processing Tasks and the Utility of Transformer Models](https://mlsanity.com/paper/2107.03844/) (2021)
- [RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers](https://mlsanity.com/paper/2310.14261/) (2023)
- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language](https://mlsanity.com/paper/2210.10903/) (2022)
- [Feature Extraction Using Deep Generative Models for Bangla Text Classification on a New Comprehensive Dataset](https://mlsanity.com/paper/2308.13545/) (2023)
- [Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection](https://mlsanity.com/paper/2311.18778/) (2023)

## Abstract (arXiv)

Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce results that can be both comparable to works done by others and reusable as stepping stones for future researchers to progress in this field. Therefore, we first tried to provide a textual dataset - that includes not just Bangla, but Romanized Bangla texts as well, is substantial, post-processed and multiple validated, ready to be 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 some experimental pre-training by using data from one validation to pre-train the other and vice versa. Lastly, we documented the results along with some analysis on them, which were promising.