---
language:
- mai
- en
- bh
license: apache-2.0
tags:
- sentiment-analysis
- text-classification
- maithili
- devanagari
- low-resource-nlp
- indic-nlp
- cross-lingual-transfer
- bihari-languages
task_categories:
- text-classification
task_ids:
- sentiment-classification
size_categories:
- 10K<n<100K
---

# Maithili 64K Dataset


# 🌾 Maithili Multi-Dimensional Sentiment Corpus

![Language](https://img.shields.io/badge/Language-Maithili-blue)
![Task](https://img.shields.io/badge/Task-Multi--Dimensional_Sentiment-green)
![Size](https://img.shields.io/badge/Size-64.2K_Rows-orange)
![Format](https://img.shields.io/badge/Format-CSV-lightgrey)

## 📌 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:
```bibtex
@article{prasad2026maithili,
  title={abhiprd20/Maithili_Sentiment_8K},
  author={Prasad, Abhimanyu},
  year={2026},
  
}