An Unsupervised Approach for Sentiment Analysis on Social Media Short Text Classification in Roman Urdu

• Published in: ACM transactions on Asian and low-resource language information processing 2022-03, Vol.21 (2), p.1-16
• Main Authors: Rana, Toqir A., Shahzadi, Kiran, Rana, Tauseef, Arshad, Ahsan, Tubishat, Mohammad
• Format: Article
• Language: English
• ISSN: 2375-4699; 2375-4702
• DOI: 10.1145/3474119

During the last two decades, sentiment analysis, also known as opinion mining, has become one of the most explored research areas in Natural Language Processing (NLP) and data mining. Sentiment analysis focuses on the sentiments or opinions of consumers expressed over social media or different web sites. Due to exposure on the Internet, sentiment analysis has attracted vast numbers of researchers over the globe. A large amount of research has been conducted in English, Chinese, and other languages used worldwide. However, Roman Urdu has been neglected despite being the third most used language for communication in the world, covering millions of users around the globe. Although some techniques have been proposed for sentiment analysis in Roman Urdu, these techniques are limited to a specific domain or developed incorrectly due to the unavailability of language resources available for Roman Urdu. Therefore, in this article, we are proposing an unsupervised approach for sentiment analysis in Roman Urdu. First, the proposed model normalizes the text to overcome spelling variations of different words. After normalizing text, we have used Roman Urdu and English opinion lexicons to correctly identify users’ opinions from the text. We have also incorporated negation terms and stemming to assign polarities to each extracted opinion. Furthermore, our model assigns a score to each sentence on the basis of the polarities of extracted opinions and classifies each sentence as positive, negative, or neutral. In order to verify our approach, we have conducted experiments on two publicly available datasets for Roman Urdu and compared our approach with the existing model. Results have demonstrated that our approach outperforms existing models for sentiment analysis tasks in Roman Urdu. Furthermore, our approach does not suffer from domain dependency.