Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model. Issue 4 (July 2020)

• Record Type: Journal Article
• Authors: Mahmood, Zainab; Safder, Iqra; Nawab, Rao Muhammad Adeel; Bukhari, Faisal; Nawaz, Raheel; Alfakeeh, Ahmed S.; Aljohani, Naif Radi; Hassan, Saeed-Ul
• Is Part Of: Information processing & management. Volume 57:Issue 4(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Roman Urdu corpus -- Urdu sentiment analysis -- Under resource language -- Deep learning -- Text Classification -- Recurrent Convolutional Neural Networks (RCNN); Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102233
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: This study contributes to generating the Roman Urdu corpus by annotating 10, 021 reviews for the task of sentiment analysis. The deep learning models such as RCNN can help in text classification task for the under resource Urdu language. The RCNN outperforms Rule-based and N-gram methods, due to its ability to learn a language-independent textual context. Abstract: Although over 64 million people worldwide speak Urdu language and are well aware of its Roman script, limited research and efforts have been made to carry out sentiment analysis and build language resources for the Roman Urdu language. This article proposes a deep learning model to mine the emotions and attitudes of people expressed in Roman Urdu - consisting of 10, 021 sentences from 566 online threads belonging to the following genres: Sports; Software; Food & Recipes; Drama; and Politics. The objectives of this research are twofold: (1) to develop a human-annotated benchmark corpus for the under-resourced Roman Urdu language for the sentiment analysis; and (2) to evaluate sentiment analysis techniques using the Rule-based, N-gram, and Recurrent Convolutional Neural Network (RCNN) models. Using Corpus, annotated by three experts to be positive, negative, and neutral with 0.557 Cohen's Kappa score, we run two sets of tests, i.e., binary classification (positive and negative) and tertiary classification (positive, negative and neutral). Finally, the results of the RCNN model are analyzed by comparing it with the outcome of the Rule-based and N-gram models. We show that the RCNN model outperforms baseline models in terms of accuracy of 0.652 for binary classification and 0.572 for tertiary classification.