raselmeya2194/Bangla-Multi-Label-Text_Analysis

This dataset is a consolidated collection of four public Bengali text datasets curated for sentiment analysis, toxic comment classification and bengali news classification. It consists of Bengali text comments annotated with multiple categories, covering a wide range of sentiment and content-based labels. Aimed at advancing research in Bengali language processing, this dataset is particularly suited for tasks like sentiment analysis, hate speech detection, and contextual comment categorization.

Categories and Labels

The dataset spans over 23 distinct categories, including:

• Sentiment Labels: Positive, Very Positive, Negative, Very Negative, Neutral
• Toxicity Labels: Hate, Vulgar, Insult, Threat, Troll
• Content Themes: Political, Religious, National, International, Sports, Entertainment, Lifestyle, Crime, Editorial, Education, Finance, Technology, Miscellaneous

Label Distribution

The dataset contains approximately 56,219 entries distributed across the following categories:

শ্রেণী (Category)	সংখ্যা (Count)
নিরপেক্ষ (Neutral)	10,536
ইতিবাচক (Positive)	9,945
নেতিবাচক (Negative)	6,505
জাতীয় (National)	5,321
খুব নেতিবাচক (Very Negative)	3,928
অশ্লীল (Vulgar)	2,505
খুব ইতিবাচক (Very Positive)	2,280
আন্তর্জাতিক (International)	1,898
ঘৃণা (Hate)	1,894
ক্রীড়া (Sports)	1,858
ট্রল (Troll)	1,389
বিবিধ (Miscellaneous)	1,236
ধর্মীয় (Religious)	1,188
সম্পাদকীয় (Editorial)	1,021
হুমকি (Threat)	916
রাজনীতি (Politics)	879
বিনোদন (Entertainment)	788
অপমান (Insult)	596
লাইফস্টাইল (Lifestyle)	342
অপরাধ (Crime)	335
শিক্ষা (Education)	308
অর্থ (Finance)	300
প্রযুক্তি (Technology)	251

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Future Work and Research Topics

1. Multi-Label Classification for Toxicity Detection in Bengali

   • Toxicity in online content often features overlapping categories (e.g., hate and insult). Developing a robust multi-label classification model to accurately differentiate among these types could improve automated content moderation for Bengali users. Future work could also explore hierarchical labeling strategies to handle nested toxicity types.

2. Transfer Learning and Domain Adaptation for Bengali Language Processing

   • Given the limited annotated resources in Bengali, leveraging transfer learning from large multilingual models (e.g., mBERT, XLM-R) could enhance performance. Exploring domain adaptation (such as fine-tuning for specific domains like news or social media comments) may yield insights into the domain-specific nuances in sentiment and toxicity classification.

3. Sentiment Analysis in Code-Mixed Language

   • Bengali users often incorporate English words or phrases, creating “code-mixed” content. This dataset could be extended to support research in Bengali-English code-mixed sentiment analysis, enabling models to effectively capture sentiment across mixed-language data—especially relevant for social media and online forums.

4. Explainable Toxicity and Sentiment Detection Models

   • Explainable models can be valuable for toxicity classification, where transparency in predictions is critical. By integrating attention mechanisms or explainability layers, researchers can develop models that highlight key words or phrases influencing each label, helping contextualize toxicity in Bengali content.

5. Time-Based Sentiment Analysis for Socio-Political Events

   • With categories like politics and national affairs, this dataset could support temporal sentiment analysis, especially around key events (e.g., elections, sports events). Time-series models could be developed to observe sentiment shifts and toxic comment trends in response to socio-political developments.

6. Low-Resource Language Model Development and Benchmarking

   • Future research could focus on developing Bengali-specific language models trained entirely on Bengali data, benchmarking architectures like BERT, LSTM, and Transformer models to identify which best serve Bengali sentiment and toxicity analysis. This work would contribute significantly to resources for low-resource language processing.

This dataset serves as a valuable asset for advancing NLP research in Bengali, supporting applications such as social media moderation, sentiment-based recommendation systems, public sentiment analysis, and automated hate speech regulation tools.

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Dataset Acknowledgments

This dataset was created by merging and preprocessing four publicly available Bengali text datasets, developed by the original authors for open-source research in Bengali language processing. We would like to acknowledge and thank the authors of these sources for their contributions, which have enabled this combined resource to support broader research applications such as sentiment analysis, hate speech regulation, and public sentiment studies in the Bengali language.

The original datasets are as follows:

1. Multi-Labeled Toxic Comments in Bengali by Tanveer Ahmed Belal, GM Shahariar, and Md Hasanul Kabir - IEEE 2023 ECCE Conference Paper

2. Bengali News Comments - Dataset Link

3. Bengali Sentiment Analysis Dataset by Salim Sazzed and Sampath Jayarathna - IEEE 2019 IRI Conference Paper

4. Bangla News Dataset

These resources have significantly contributed to advancing Bengali NLP research and development, particularly in the realm of sentiment analysis and toxic comment classification.

Citation

If you are using this dataset, please cite it as follows:

@misc{md__rasel_meya_2024,
  title={Bengali Text Classification Dataset},
  url={https://www.kaggle.com/dsv/9720472},
  DOI={10.34740/KAGGLE/DSV/9720472},
  publisher={Kaggle},
  author={Md. Rasel Meya},
  year={2024}
}