A machine learning approach for Urdu text sentiment analysis

Abstract

Product evaluations, ratings, and other sorts of online expressions have risen in popularity as a result of the emergence of social networking sites and blogs. Sentiment analysis has emerged as a new area of study for computational linguists as a result of this rapidly expanding data set. From around a decade ago, this has been a topic of discussion for English speakers. However, the scientific community completely ignores other important languages, such as Urdu. Morphologically, Urdu is one of the most complex languages in the world. For this reason, a variety of unique characteristics, such as the language's unusual morphology and unrestricted word order, make the Urdu language processing a difficult challenge to solve. This research provides a new framework for the categorization of Urdu language sentiments. The main contributions of the research are to show how important this multidimensional research problem is as well as its technical parts, such as the parsing algorithm, corpus, lexicon, etc. A new approach for Urdu text sentiment analysis including data gathering, pre-processing, feature extraction, feature vector formation, and finally, sentiment classification has been designed to deal with Urdu language sentiments. The result and discussion section provides a comprehensive comparison of the proposed work with the standard baseline method in terms of precision, recall, f-measure, and accuracy of three different types of datasets. In the overall comparison of the models, the proposed work shows an encouraging achievement in terms of accuracy and other metrics. Last but not least, this section also provides the featured trend and possible direction of the current work.

CNN and LSTM based hybrid deep learning model for sentiment analysis on Arabic text reviews

“…Numerous studies are dedicated to computing-based classification, particularly within the domain of Sentiment Analysis on different language such as Urdu [14], Chinese [15], Arabic [16], etc. Many works focus on word polarity as a basis for sentiment analysis [1][3] [17].…”

Sentiment Analysis Based on Urdu Reviews Using Hybrid Deep Learning Models

“…This work aims to develop a new SA review method based on the Urdu language. To improve the accuracy of Urdu SA methods, it is also necessary to reinforce the sensation technique [10].…”

“…Akhtar et al [10] offered a fresh paradigm for classifying sentiments expressed in Urdu. The primary contributions of the research demonstrate the significance of this multifaceted research challenge and its technical components, including the lexicon, corpus, and parsing algorithm.…”