Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning  

作　　者：Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini 

机构地区：[1]Department of IT and Computer Science,Pak-Austria Fachhochschule:Institute of Applied Sciences and Technology,Haripur,22620,Pakistan [2]Software Competence Center Hagenberg,Softwarepark 32a,Hagenberg,4232,Austria [3]Department of Computer Science,School of Engineering and Digital Sciences,Nazarbayev University,Astana,010000,Kazakhstan [4]Department of Information Technology,College of Computer,Qassim University,P.O.Box 1162,Buraydah,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第4期713-733,共21页计算机、材料和连续体（英文）

摘　　要：Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse languages.While numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiveness of the models. The research findings rev

关 键 词：Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP181[自动化与计算机技术—计算机科学与技术]