A Bidirectional Long Short Memory Network for Roman Urdu Using Novel Dataset
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Date
2022
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Publisher
UMT, Lahore
Abstract
The introduction of the internet made possible the quick and easy dissemination of information about a wide variety of topics, including products, administrations, events, and political hypotheses, among others. Although there has been a rapid increase in the number of research undertaken on sentiment analysis, the majority of these studies have focused on issues associated with the English dialect. It is more challenging to do sentiment analysis in Roman Urdu than it is in English for a number of different reasons. Due to Roman Urdu's lack of distinct lexical resources, there is a possibility that information might get mixed. The primary purpose of this study is to build a large dataset for doing sentiment analysis in Roman Urdu, and a secondary objective is to evaluate several approaches to implementing such analysis by making use of machine learning and deep learning models. The approaches for analysing Roman and Urdu sentiments that are highlighted in this research are the ones that are used most often and extensively. The findings of this research will enhance the resource that is Roman Urdu as well as the methods that are used in sentiment analysis. For the sake of study on Roman Urdu, a dataset is generated. In order to achieve the highest possible levels of accuracy and performance, a combination of machine learning and deep learning algorithms is used. Our proposed approach achieves an accuracy of 83% in machine learning and 70% in deep learning, respectively, on the test data