Mukaffi28/Motamot_Bengali_Political_Sentiment_Analysis

Motamot: Bengali Political Sentiment Analysis Dataset

📖 Overview

Motamot is a Bengali political sentiment analysis dataset containing 7,058 labeled data points. Each entry is annotated with Positive or Negative sentiment, specifically tailored for analyzing political discourse in the Bengali language.

This dataset supports Natural Language Processing (NLP) research, with applications in sentiment classification, political opinion mining, and benchmarking pre-trained and large language models (LLMs) for low-resource languages.

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📊 Dataset Statistics

Split	Total	Positive	Negative
Train	5647	3306	2341
Test	706	413	293
Validation	705	413	292
Total	7058	4132	2926

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📜 Citation

If you use this dataset, please cite the following paper:

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@INPROCEEDINGS{10752197,
  author={Johora Faria, Fatema Tuj and Moin, Mukaffi Bin and Mumu, Rabeya Islam and Alam Abir, Md Mahabubul and Alfy, Abrar Nawar and Alam, Mohammad Shafiul},
  booktitle={2024 IEEE Region 10 Symposium (TENSYMP)}, 
  title={Motamot: A Dataset for Revealing the Supremacy of Large Language Models Over Transformer Models in Bengali Political Sentiment Analysis}, 
  year={2024},
  pages={1-8},
  keywords={Sentiment analysis;Analytical models;Accuracy;Voting;Large language models;Transformers;Market research;Few shot learning;Portals;IEEE Regions;Political Sentiment Analysis;Pre-trained Language Models;Large Language Models;Gemini 1.5 Pro;GPT 3.5 Turbo;Zero-shot Learning;Fewshot Learning;Low-resource Language},
  doi={10.1109/TENSYMP61132.2024.10752197}
}

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📂 Dataset Structure

Motamot/
│
├── train.csv        # Training set (5,647 instances)
├── test.csv         # Test set (706 instances)
├── validation.csv   # Validation set (705 instances)

Each file contains:

• text → Bengali political statement
• label → Sentiment category (Positive or Negative)

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🧪 Baseline Results

🔹 Comparative Analysis of Pre-trained Language Models

Model	Accuracy	Precision	Recall	F1-Score
BanglaBERT	0.8204	0.8222	0.8204	0.8203
Bangla BERT Base	0.6803	0.6907	0.6812	0.6833
DistilBERT	0.6320	0.6358	0.6320	0.6317
mBERT	0.6427	0.6496	0.6428	0.6153
sahajBERT	0.6708	0.6791	0.6709	0.6707

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🔹 Comparative Analysis of Large Language Models (Few-shot & Zero-shot)

Model (LLM)	Metric	Zero-shot	5-shot	10-shot	15-shot
GPT 3.5 Turbo	Accuracy	0.8500	0.8900	0.9133	0.9400
	Precision	0.8467	0.8867	0.9200	0.9467
	Recall	0.8533	0.8926	0.9079	0.9342
	F1-Score	0.8495	0.8896	0.9139	0.9404
Gemini 1.5 Pro	Accuracy	0.8608	0.8981	0.9200	0.9633
	Precision	0.8931	0.8846	0.9333	0.9667
	Recall	0.8477	0.9205	0.9091	0.9603
	F1-Score	0.8698	0.9022	0.9211	0.9635

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📬 Contact Information

For questions, collaborations, or inquiries:

• Mukaffi Bin Moin — 📧 mukaffi28@gmail.com
• Fatema Tuj Johora Faria — 📧 fatema.faria142@gmail.com
• Rabeya Islam Mumu — 📧 rabeya.islammomo@gmail.com
• Md Mahabubul Alam Abir — 📧 mahbubabir09@gmail.com
• Abrar Nawar Alfy — 📧 abraralfy49@gmail.com

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