Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs

TL;DR

This paper introduces a new colloquial Persian sentiment dataset from social microblogs and proposes a CNN-based architecture that significantly improves sentiment classification accuracy for informal Persian texts.

Contribution

It constructs the first large-scale colloquial Persian sentiment dataset and develops a CNN-based model tailored for social media text analysis in low-resource languages.

Findings

Constructed a 60,000-item Persian social microblog dataset.

Achieved 72% accuracy with the proposed CNN model.

Demonstrated improved sentiment classification performance.

Abstract

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.