Multi-class sentiment analysis of urdu text using multilingual BERT

Sentiment analysis (SA) is an important task because of its vital role in analyzing people's opinions. However, existing research is solely based on the English language with limited work on low-resource languages. This study introduced a new multi-class Urdu dataset based on user reviews for sentiment analysis. This dataset is gathered from various domains such as food and beverages, movies and plays, software and apps, politics, and sports. Our proposed dataset contains 9312 reviews manually annotated by human experts into three classes: positive, negative and neutral. The main goal of this research study is to create a manually annotated dataset for Urdu sentiment analysis and to set baseline results using rule-based, machine learning (SVM, NB, Adabbost, MLP, LR and RF) and deep learning (CNN-1D, LSTM, Bi-LSTM, GRU and Bi-GRU) techniques. Additionally, we fine-tuned Multilingual BERT(mBERT) for Urdu sentiment analysis. We used four text representations: word n-grams, char n-grams,pre-trained fastText and BERT word embeddings to train our classifiers. We trained these models on two different datasets for evaluation purposes. Finding shows that the proposed mBERT model with BERT pre-trained word embeddings outperformed deep learning, machine learning and rule-based classifiers and achieved an F1 score of 81.49%.