language:
- bn
tags:
- toxic comments
size_categories:
- 10K<n<100K
Dataset Card for Dataset Name
Dataset Description
- Homepage:
- Repository:
- Toxic-Comment-Detection-BN
- Paper:
- Bangla Toxic Comment Classification and Severity Measure Using Deep Learning
- Leaderboard:
- Point of Contact:
Dataset Summary
Since the deep learning approach needs a huge number
of data for model training so it was a major challenge for
us to collect a large amount of data to train our model.
Some sample comments that we have collected are given
below:
ছাগেলর বাƐা ছাগল
েদেখ পুড়াই িহজড়ার মেতা েদখেত
পাডার েপা পাডা েতাের ময্ানেহােল ডু বাইয়া মারেত পারতাম যিদ
We have collected almost 4141 labeled data from the
previous work of Bangla toxic comment by Jubaer et al.
[6], which are described in table 1. For more data, we have
collected a total of 22, 000 comments have been collected
from Tiktok, the majority of which are toxic comments.
Our experts labeled these comments based on 6 categories
that are not mutually exclusive. All the annotators are
given clear guidelines on how to rate these comments.
The guidelines can be summarized in Table I.
The annotated comments are cleaned by removing
emoticons, unnecessary punctuation marks, characters,
digits, and other symbols as they contribute very little
to the context of the comments.
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Source Data
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Annotations
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