# Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs

arXiv: https://arxiv.org/abs/2306.12679 · HTML: https://mlsanity.com/paper/2306.12679/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Probabilistic FastText for Multi-Sense Word Embeddings](https://mlsanity.com/paper/1806.02901/) (2018)
- [SentiPers: A Sentiment Analysis Corpus for Persian](https://mlsanity.com/paper/1801.07737/) (2018)
- [A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks](https://mlsanity.com/paper/1909.13568/) (2019)
- [A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods](https://mlsanity.com/paper/2104.14751/) (2021)
- [Exploiting Deep Learning for Persian Sentiment Analysis](https://mlsanity.com/paper/1808.05077/) (2018)
- [LexiPers: An ontology based sentiment lexicon for Persian](https://mlsanity.com/paper/1911.05263/) (2019)
- [A Context-based Disambiguation Model for Sentiment Concepts Using a Bag-of-concepts Approach](https://mlsanity.com/paper/2008.03020/) (2020)
- [Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews](https://mlsanity.com/paper/2109.07680/) (2021)

## Built on by (most cited first, 2 of 2)

- [Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification](https://mlsanity.com/paper/2403.06023/) (2024)
- [Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts](https://mlsanity.com/paper/2602.21214/) (2026)

## Abstract (arXiv)

Introduction: Microblogging websites have massed rich data sources for sentiment analysis and opinion mining. In this regard, sentiment classification has frequently proven inefficient because microblog posts typically lack syntactically consistent terms and representatives since users on these social networks do not like to write lengthy statements. Also, there are some limitations to low-resource languages. The Persian language has exceptional characteristics and demands unique annotated data and models for the sentiment analysis task, which are distinctive from text features within the English dialect. Method: This paper first constructs a user opinion dataset called ITRC-Opinion in a collaborative environment and insource way. Our dataset contains 60,000 informal and colloquial Persian texts from social microblogs such as Twitter and Instagram. Second, this study proposes a new architecture based on the convolutional neural network (CNN) model for more effective sentiment analysis of colloquial text in social microblog posts. The constructed datasets are used to evaluate the presented architecture. Furthermore, some models, such as LSTM, CNN-RNN, BiLSTM, and BiGRU with different word embeddings, including Fasttext, Glove, and Word2vec, investigated our dataset and evaluated the results. Results: The results demonstrate the benefit of our dataset and the proposed model (72% accuracy), displaying meaningful improvement in sentiment classification performance.