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

Computers, Materials & Continua 2024年第4期79卷 713-733页

作者： aizaz ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** 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 effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Effect of phosphodiesterase inhibitors in the bladder

引用

Asian Journal of Urology 2015年第1期2卷 33-37页

作者： Bilal Chughtai aizaz ali Claire Dunphy Steven A.Kaplan Weill Cornell Medical College New York CityNYUSA

Many aging men will experience lower urinary tract symptoms(LUTS).Phosphodiesterase type 5(PDE5)inhibitors have shown promise in treating LUTS in these ***5 inhibitors mediate their effects through several pathways including cAMP,NO/cGMP,Kchannel modulated pathways,and the L-cysteine/H2S ***5 inhibitors exert their effect in muscle cells,nerve fibers,and interstitial cells(ICs).The use of PDE5 inhibitors led to improvement in *** included urodynamic ***5 inhibitors may play a significant role in LUTS due to their effect on the bladder rather than the prostate.

关键词： Lower urinary tract symptoms(LUTS) Overactive bladder syndrome(OAB) Phosphodiesterase type 5 inhibitors NO/cGMP cAMP

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