DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus

Sentiment Analysis in Persian Using Deep Neural Networks

Binary and multiclass sentiment detection using deep neural architectures (BLSTM and CNN) on Persian augmented texts

NB: DeepSentiPers is a modified version of our paper presented at the fifth computational linguistics conference in Iran.
https://arxiv.org/pdf/2004.05328.pdf

This study uses deep neural networks to extract opinions over each Persian sentence-level text. Deep learning models provided a new way to boost the quality of the output. However, these architectures need to feed on big annotated data and be made from an accurate design. To the best of our knowledge, we do not merely suffer from a lack of well-annotated Persian sentiment corpus but also a novel model to classify Persian opinions for multiple and binary classification. Thus, our study first proposes two novel deep learning architectures comprised of bidirectional LSTM and CNN. These architectures are a part of a deep neural hierarchy designed precisely and can classify sentences for both tasks. Second, we suggest three data augmentation techniques for the low-resources Persian sentiment corpus. Our comprehensive experiments on three baselines and two different neural word embedding methods show that our data augmentation methods and intended models successfully address the research aims.

Results

Overall the DeepSentiPers achieved the following results in the Persian sentiment analysis task. H/E, read the paper to find out more about the results.

Classification-Type	BLSTM F1-Score	Word-Embedding	Data-Augmentation
Binary	91.98	Keras	Translation
Multi-Class	69.33	FastText	Translation

Citation

Please cite the arXiv paper if you use DeepSentiPers in your work:

@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}
}

Getting started

All the things you need to work on this project is an Ipython environment like the Google Colab or Jupyter and the dataset files.

Dataset

The dataset used in this project was collected from SentiPers corpus, containing 7419 Persian sentences and their corresponding polarity. The original and augmented dataset files are accessible in the "Dataset" folder.

Authors

• Javad Pourmostafa - GitHub, LinkedIn, ResearchGate, Website
• Parsa Abbasi - GitHub, LinkedIn, ResearchGate, Website
• Seyed Abolghasem Mirroshandel LinkedIn, ResearchGate, Website

Miscellaneous

See also the list of contributors who participated in this project.

We're glad to announce that the DeepSentiPers has also been written in Persian. You can find it here: https://zenodo.org/record/3551273. Please note that the intended version is slightly different from the English version.

Persian Title: ارائه یک سیستم تحلیل احساس در زبان فارسی با استفاده از مدل های یادگیری عمیق