Sentiment Analysis of Code-Mixed Roman Urdu-English Social Media Text using Deep Learning Approaches

Abstract

Sentiment analysis is the computational study of attitudes, opinions, and sentiments towards certain issues, products, individuals, and organizations. Companies and customers are making decisions by seeking opinions from social media text. Sentiment analysis is getting intelligent with the advancement of artificial intelligence and natural language processing. With a stunning increase in the use of social media, a huge volume of text available on these platforms is in imperfect and informal languages like Roman Urdu mixed with the English language. Present sentiment analysis techniques do not perform precisely on these code-mixed imperfect, informal, and poorly resourced languages. A promising solution is the use of deep learning models on these code-mixed Roman Urdu and English text. Therefore, the objective of this paper is to perform a sentiment analysis of code-mixed Roman Urdu and English social media text using state-of-the-art deep learning models. Our work is independent of lexical normalization, language dictionary, and code transfer indication. We perform sentiment analysis using Multilingual BERT (mBERT) and XLM-RoBERTa (XLM-R) models. The results reveal that performance of XLM-R model with tuned hyperparameters for code-mixed Roman Urdu and English social media text is better than the mBERT model with F1 score of 71%.