abhiprd20/Maithili_Sentiment_8K

Maithili 64K Dataset

🌾 Maithili Multi-Dimensional Sentiment Corpus

📌 Executive Summary

Standard sentiment analysis in Indian vernaculars relies on flat, one-dimensional classification. The Maithili Multi-Dimensional Sentiment Corpus (64,215 rows) introduces a high-resolution, socio-linguistically grounded architecture. It utilizes a dual-axis classification system—predicting both primary sentiment and emotional intensity—mapped across highly specific demographic speaker profiles and rural domains.

🧠 Architectural Innovations

1. Dual-Axis Taxonomy (Sentiment + Intensity)

Models often fail to distinguish between minor inconveniences and severe grievances in low-resource settings. This dataset introduces the sentiment_intensity vector:

• Mild (22,782 samples)
• Moderate (23,224 samples)
• Strong (18,209 samples) This allows researchers to train models with a nuanced loss function that scales based on the severity of the utterance.

2. Socio-Linguistic Anchoring (speaker_type)

Maithili morphology shifts drastically based on profession and generation. Utterances are explicitly categorized by speaker_type (e.g., Farmer, Teacher, Elder, Student). This metadata enables the measurement and mitigation of demographic and generational bias in LLM generations.

3. Template-Anchored Rural Elicitation

To combat the urban/social-media bias prevalent in web-scraped data, this corpus utilizes a template-anchored elicitation protocol. Translations are anchored around specific geographic entities (e.g., Khagaria, Muzaffarpur, Jamui) and underrepresented rural domains (Agriculture, Livestock, Health), ensuring the LLM learns to map sentiment to tangible local realities.

📊 Dataset Schema

• id: Unique identifier.
• text: The Maithili utterance.
• english_translation: English semantic equivalent.
• label: Primary sentiment (positive, negative, neutral).
• sentiment_intensity: Intensity of the sentiment (mild, moderate, strong).
• domain: e.g., agriculture, health, economy.
• speaker_type: Demographic origin of the syntax (e.g., farmer, housewife).

⚙️ Intended Use & Limitations

• Best For: Intensity regression tasks, socio-linguistic bias evaluation, and sentiment classification in rural/agricultural domains.
• Limitations: Due to the template-anchored elicitation used to guarantee geographical coverage, the syntactic variance is lower than highly spontaneous conversational data. It is optimal for representation learning rather than open-ended generative chat.

📝 Citation

If you use this dataset in your research, please cite the accompanying paper:

@article{prasad2026maithili,
  title={abhiprd20/Maithili_Sentiment_8K},
  author={Prasad, Abhimanyu},
  year={2026},
  
}