Persian Sentiment Analysis

Table of contents

1. SnappFood
2. Digikala Sentiment
3. DeepSentiPers Sentiment

SnappFood

Snappfood (an online food delivery company) user comments containing 70,000 comments with two labels (i.e. polarity classification):

1. Happy
2. Sad

Download You can download the dataset from here

Cite

Please cite the following paper in your publication if you are using this dataset in your research:

@article{ParsBERT,
    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
    journal={ArXiv},
    year={2020},
    volume={abs/2005.12515}
}

Digikala Sentiment

Digikala user comments provided by Open Data Mining Program (ODMP). This dataset contains 62,321 user comments with three labels:

Download You can download the dataset from here

Cite

Please cite the following paper in your publication if you are using this dataset in your research:

@misc{digikalanext, 
    title={DigikalaNext: Open Data Mining Program}, 
    url={https://www.digikala.com/opendata/}, 
    journal={DigikalaNext | Open Data Mining Program}
}

DeepSentiPers Sentiment

which is a balanced and augmented version of SentiPers, contains 12,138 user opinions about digital products labeled with five different classes; two positives (i.e., happy and delighted), two negatives (i.e., furious and angry) and one neutral class. Therefore, this dataset can be utilized for both multi-class and binary classification. In the case of binary classification, the neutral class and its corresponding sentences are removed from the dataset.

Binary:

1. Negative (Furious + Angry)
2. Positive (Happy + Delighted)

Multi

1. Furious
2. Angry
3. Neutral
4. Happy
5. Delighted

Download You can download the dataset from:

• SentiPers
• DeepSentiPers

Cite

Please cite the following paper in your publication if you are using this dataset in your research:

@misc{sharami2020deepsentipers,
    title={DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus},
    author={Javad PourMostafa Roshan Sharami and Parsa Abbasi Sarabestani and Seyed Abolghasem Mirroshandel},
    year={2020},
    eprint={2004.05328},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
@article{hosseini2018sentipers,
  title={SentiPers: A sentiment analysis corpus for Persian},
  author={Hosseini, Pedram and Ramaki, Ali Ahmadian and Maleki, Hassan and Anvari, Mansoureh and Mirroshandel, Seyed Abolghasem},
  journal={arXiv preprint arXiv:1801.07737},
  year={2018}
}