# Vulgar Remarks Detection in Chittagonian Dialect of Bangla

arXiv: https://arxiv.org/abs/2308.15448 · HTML: https://mlsanity.com/paper/2308.15448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 97 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling](https://mlsanity.com/paper/1611.06639/) (2016)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Hate Speech and Offensive Language Detection in Bengali](https://mlsanity.com/paper/2210.03479/) (2022)
- [BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs](https://mlsanity.com/paper/1704.06125/) (2017)
- [Hate Speech Detection in Roman Urdu](https://mlsanity.com/paper/2108.02830/) (2021)
- [Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis](https://mlsanity.com/paper/1505.07302/) (2015)
- [Bangla Text Dataset and Exploratory Analysis for Online Harassment Detection](https://mlsanity.com/paper/2102.02478/) (2021)
- [Enhanced Twitter Sentiment Classification Using Contextual Information](https://mlsanity.com/paper/1605.05195/) (2016)
- [Training Cross-Lingual embeddings for Setswana and Sepedi](https://mlsanity.com/paper/2111.06230/) (2021)

## Abstract (arXiv)

The negative effects of online bullying and harassment are increasing with Internet popularity, especially in social media. One solution is using natural language processing (NLP) and machine learning (ML) methods for the automatic detection of harmful remarks, but these methods are limited in low-resource languages like the Chittagonian dialect of Bangla.This study focuses on detecting vulgar remarks in social media using supervised ML and deep learning algorithms.Logistic Regression achieved promising accuracy (0.91) while simple RNN with Word2vec and fastTex had lower accuracy (0.84-0.90), highlighting the issue that NN algorithms require more data.