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RUSAS: Roman Urdu sentiment analysis System

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Computers, Materials & Continua 2024年第4期79卷 1463-1480页

作者： Kazim Jawad Muhammad Ahmad Majdah Alvi Muhammad Bux Alvi Faculty of Computer Science and Engineering Frankfurt University of Applied SciencesFrankfurt am Main60318Germany Technical Writer and Researcher Proteus Technologies LLCIslamabad04405Pakistan Faculty of Engineering The Islamia University of BahawalpurBahawalpur63100Pakistan

sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify thesentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internetusing various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer tocommunicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language *** have developed versatile lexical resources for features-rich comprehensive languages, but limitedlinguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompassesextracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. Theprimary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. Thecontributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman UrduSpelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct thespellings, categorize the sentiments, and return the input sentence’s orientation with a sentiment intensity *** developed system gains strength with each input experience gradually. The results show that the languagedetector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of94.3%.

关键词： Roman Urdu sentiment analysis Roman Urdu language detector Roman Urdu spelling checker flask

Public Opinions on ChatGPT:An analysis of Reddit Discussions by Using sentiment analysis,Topic Modeling,and SWOT analysis

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Data Intelligence 2024年第2期6卷 344-374页

作者： Shwe Zin Su Naing Piyachat Udomwong International College of Digital Innovation and Financial Technology 239 Nimmanhaemin Road SuthepMuangChiang Mai 50200Thailand

The sudden arrival of Al(Artificial Intelligence)into people's daily lives all around the world was marked by the introduction of ChatGPT,which was officially released on November 30,*** Al invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields,as its capacity extends across various ***,numerous articles and journals have been discussing ChatGPT,making it a headline for several ***,it does not reflect most public opinion about the ***,this paper investigated the public's opinions on ChatGPT through topic modelling,Vader-based sentiment analysis and SWOT *** gather data for this study,202905 comments from the Reddit platform were collected between December 2022 and December *** findings reveal that the Reddit community engaged in discussions related to ChatGPT,covering a range of topics including comparisons with traditional search engines,the impacts on software development,job market,and education industry,exploring ChatGPT's responses on entertainment and politics,the responses from Dan,the alter ego of ChatGPT,the ethical usage of user data as well as queries related to the Al-generated *** sentiment analysis indicates that most people hold positive views towards this innovative technology across these several ***,concerns also arise regarding the potential negative impacts associated with this *** SWOT analysis of these results highlights both the strengths and pain points,market opportunities and threats associated with *** analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

关键词： ChatGPT sentiment analysis Topic modeling SWOT analysis Public opinion Reddit

DeBERTa-GRU: sentiment analysis for Large Language Model

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Computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

sentiment analysis and Classification Using Deep Semantic Information and Contextual Knowledge

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Computers, Materials & Continua 2023年第1期74卷 671-691页

作者： Ahmed Abdulhakim Al-Absi Dae-Ki Kang Mohammed Abdulhakim Al-Absi Department of Smart Computing Kyungdong University 464-gilBongpoGosungGangwon-do24764Korea Division of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan47011Korea

sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and ***-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low *** this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given *** first temporal relationships are established then deep semantic knowledge and contextual knowledge are ***,a deep integration layer is introduced to measure the importance of features for efficient extraction of different *** of DSCNet model lies in introducing the deep *** is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 ***,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 *** achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.

关键词： sentiment analysis aspect-sa deep learning DSCNet

sentiment analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

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Computer Modeling in Engineering & Sciences 2022年第6期131卷 1263-1287页

作者： Bilal Chandio Asadullah Shaikh Maheen Bakhtyar Mesfer Alrizq Junaid Baber Adel Sulaiman Adel Rajab Waheed Noor Department of Computer Science and Information Technology University of BalochistanQuetta87300Pakistan College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia Department of Information Technology University of BalochistanQuetta87300Pakistan

sentiment analysis task has widely been studied for various languages such as English and ***,Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing(NLP)*** primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically *** mitigate this challenge,we propose a fine-tuned Support Vector Machine(SVM)powered by Roman Urdu *** our proposed scheme,the corpus data is initially cleaned to remove the anomalies from the *** initial pre-processing,each user review is being *** input text is transformed into a feature vector using the bag-of-word ***,the SVM is used to classify and detect user *** proposed scheme is based on a dictionary based Roman Urdu *** creation of the Roman Urdu stemmer is aimed at standardizing the text so as to minimize the level of *** efficacy of our proposed model is also empirically evaluated with diverse experimental configurations,so as to fine-tune the hyper-parameters and achieve superior ***,a series of experiments are conducted on diverse machine learning and deep learning models to compare the performance with our proposed *** also introduced the largest dataset on Roman Urdu,i.e.,Roman Urdu e-commerce dataset(RUECD),which contains 26K+user reviews annotated by the group of *** RUECD is challenging and the largest dataset available of Roman *** experiments show that the newly generated dataset is quite challenging and requires more attention from the peer researchers for Roman Urdu sentiment analysis.

关键词： sentiment analysis Roman Urdu machine learning SVM

sentiment analysis in Social Media for Competitive Environment Using Content analysis

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Computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： Shahid Mehmood Imran Ahmad Muhammad Adnan Khan Faheem Khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

sentiment analysis on Social Media Using Genetic Algorithm with CNN

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Computers, Materials & Continua 2022年第3期70卷 5399-5419页

作者： Dharmendra Dangi Amit Bhagat Dheeraj Kumar Dixit Department of Mathematics and Computer Applications Maulana Azad National Institute of Technology(MANIT)BhopalMadhya PradeshIndia

There are various intense forces causing customers to use evaluated data when using social media platforms and microblogging ***,customers throughout the world share their points of view on all kinds of topics through these *** massive volume of data created by these customers makes it impossible to analyze such data ***,an efficient and intelligent method for evaluating social media data and their divergence needs to be ***,various types of equipment and techniques are available for automatically estimating the classification of *** analysis involves determining people’s emotions using facial *** analysis can be performed for any individual based on specific *** present study describes the analysis of an image dataset using CNNswithPCA intended to detect people’s sentiments(specifically,whether a person is happy or sad).This process is optimized using a genetic algorithm to get better ***,a comparative analysis has been conducted between the different models generated by changing the mutation factor,performing batch normalization,and applying feature reduction using *** steps are carried out across five experiments using *** maximum accuracy obtained is 96.984%,which is associated with the Happy and Sad sentiments.

关键词： sentiment analysis convolutional neural networks facial expression genetic algorithm

sentiment analysis with Tweets Behaviour in Twitter Streaming API

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Computer Systems Science & Engineering 2023年第5期45卷 1113-1128页

作者： Kuldeep Chouhan Mukesh Yadav Ranjeet Kumar Rout Kshira Sagar Sahoo NZ Jhanjhi Mehedi Masud Sultan Aljahdali Computer Science and Engineering I.T.S Engineering CollegeGreater Noida201310India Computer Science and Engineering DPG Institute of Technology and ManagementGurgaon122004India Computer Science and Engineering National Institute of Technology SrinagarJammu and KashmirIndia Department of Computer Science and Engineering SRM UniversityAmaravatiAndhra Pradesh522240India School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social *** researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder *** sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning *** assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,*** addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral *** this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account *** distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

关键词： Machine learning Naive Bayes natural language processing sentiment analysis social media analytics support vector machine Twitter application programming interface

Improving sentiment analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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Computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： sentiment analysis social media election prediction machine learning transformers