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metadata
license: odbl
task_categories:
  - text-classification
language:
  - fa
pretty_name: Persian Tweets - Sentiment Analysis

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

Dataset Details

Dataset Description

This dataset contains more than 3300 Persian tweets, crawled from X.com Each tweet is assigned a label, which is a number between 0 to 4. Label 0 indicates the sentiment of Happiness and Joy. Label 1 indicates the sentiment of Sadness. Label 2 indicates the sentiment of Anger and Furiosity. Label 3 indicates the sentiment of Neutral. And finally, label 4 indicates the sentiment of intense emotions, such as Surprise, Fear, and Love.

  • Curated by: Mohammadali Mohammadkhani - Sina Elahimanesh
  • Funded by : Mohammadali Mohammadkhani - Sina Elahimanesh
  • Language(s) (NLP): Persian
  • License: odbl

Direct Use

Transformers Dataset

from datasets import load_dataset

# Login using e.g. `huggingface-cli login` to access this dataset
ds = load_dataset("moali-mkh-2000/PersianTwitterDataset-SentimentAnalysis")

Pandas

import pandas as pd

# Login using e.g. `huggingface-cli login` to access this dataset
df = pd.read_csv("hf://datasets/moali-mkh-2000/PersianTwitterDataset-SentimentAnalysis/PersianTwitterDataset.csv")

Dataset Creation

Source Data

Tweets are extracted from Persian-speaking accounts of X community.

Who are the annotators?

Mohammadali Mohammadkhani - Sina Elahimanesh

Privacy and Ethical Concerns

The tweets are collected from public accounts (at the time of dataset creation). We made sure to address all privacy concerns in the process of creating this dataset.

Citation

BibTeX:

@misc{elahimanesh2025emotionalignmentdiscoveringgap,
      title={Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images}, 
      author={Sina Elahimanesh and Mohammadali Mohammadkhani and Shohreh Kasaei},
      year={2025},
      eprint={2504.10662},
      archivePrefix={arXiv},
      primaryClass={cs.HC},
      url={https://arxiv.org/abs/2504.10662}, 
}