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Analyzing Arabic Twitter-Based Patient Experience sentiments Using Multi-Dialect Arabic Bidirectional Encoder Representations from Transformers

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Computers, Materials & Continua 2023年第7期76卷 195-220页

作者： Sarab AlMuhaideb Yasmeen AlNegheimish Taif AlOmar Reem AlSabti Maha AlKathery Ghala AlOlyyan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 266Riyadh 11362Saudi Arabia

Healthcare organizations rely on patients’feedback and experiences to evaluate their performance and services,thereby allowing such organizations to improve inadequate services and address any shortcomings.According to the literature,social networks and particularly Twitter are effective platforms for gathering public opinions.Moreover,recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors,including healthcare.The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus.The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to healthcare organizations in Saudi Arabia from 1 January 2008 to 29 January 2022.The tweets were labeled according to sentiment(positive or negative)and sector(public or private),and thereby the Hospital Patient Experiences in Saudi Arabia(HoPE-SA)dataset was produced.A simple statistical analysis was conducted to examine differences in patient views of healthcare sectors.The authors trained five models to distinguish sentiments in tweets automatically with the following schemes:a transformer-based model fine-tuned with deep learning architecture and a transformer-based model fine-tuned with simple architecture,using two different transformer-based embeddings based on Bidirectional Encoder Representations from Transformers(BERT),Multi-dialect Arabic BERT(MAR-BERT),and multilingual BERT(mBERT),as well as a pretrained word2vec model with a support vector machine classifier.This is the first study to investigate the use of a bidirectional long short-term memory layer followed by a feedforward neural network for the fine-tuning of MARBERT.The deep-learning fine-tuned MARBERT-based model—the authors’best-performing model—achieved accuracy,micro-F1,and macro-F1 scores of 98.71%,98.73%,and 98.63%,respectively.

关键词： Sentiment analysis patient experience healthcare Twitter MARBERT bidirectional long short-term memory support vector machine transformer-based learning deep learning

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Deep Learning with Natural Language Processing Enabled sentimental analysis on Sarcasm Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2553-2567页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl HofufAl-Ahsa31982Saudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual data is a major challenge in the efficiency of the SA.Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of sarcasm.With the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted feature.In this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm classification.The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data.Besides,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and classification.Initially,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and tokenization.Secondly,the transformation of feature vectors takes place using the N-gram feature vector technique.Finally,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of sarcasm.To verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

关键词： Sentiment analysis sarcasm detection deep learning natural language processing n-grams hyperparameter tuning

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An analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion

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Computer Modeling in Engineering & Sciences 2023年第6期135卷 2349-2371页

作者： Mingyong Li Lirong Tang Longfei Ma Honggang Zhao Jinyu Hu Yan Wei College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing401331China

The learning status of learners directly affects the quality of learning.Compared with offline teachers,it is difficult for online teachers to capture the learning status of students in the whole class,and it is even more difficult to continue to pay attention to studentswhile teaching.Therefore,this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion.Specifically,a facial expression recognition model and an eye state recognition model are constructed to detect students’emotions and fatigue,respectively.By integrating the detected data with the homework test score data after online learning,an analysis model of students’online learning status is constructed.According to the PAD model,the learning state is expressed as three dimensions of students’understanding,engagement and interest,and then analyzed from multiple perspectives.Finally,the proposed model is applied to actual teaching,and procedural analysis of 5 different types of online classroom learners is carried out,and the validity of the model is verified by comparing with the results of the manual analysis.

关键词： Deep learning fatigue detection facial expression recognition Sentiment analysis information fusion

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Multi-Model Fusion Framework Using Deep Learning for Visual-Textual sentiment Classification

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Computers, Materials & Continua 2023年第8期76卷 2145-2177页

作者： Israa K.Salman Al-Tameemi Mohammad-Reza Feizi-Derakhshi Saeed Pashazadeh Mohammad Asadpour Computerized Intelligence Systems Laboratory Department of Computer EngineeringFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran Department of Computer Engineering Faculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran State Company for Engineering Rehabilitation and Testing Iraqi Ministry of Industry and MineralsBaghdad10011Iraq

Multimodal Sentiment analysis(SA)is gaining popularity due to its broad application potential.The existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple modalities.Moreover,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification results.Motivated by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual content.The proposed model comprises three deep neural networks.Two different neural networks are proposed to extract the most emotionally relevant aspects of image and text data.Thus,more discriminative features are gathered for accurate sentiment classification.Then,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment classification.Finally,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed model.An interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and resilience.The proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment analysis(MVSA)dataset.These results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation criteria.

关键词： Sentiment analysis multimodal classification deep learning joint fusion decision fusion interpretability

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An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 131-154页

作者： Joseph Bamidele Awotunde Sanjay Misra Vikash Katta Oluwafemi Charles Adebayo Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Computer Science and Communication Otfold University CollegeHaldenNorway

The task of classifying opinions conveyed in any form of text online is referred to as Sentiment analysis.The emergence of social media usage and its spread has given room for Sentiment analysis in our daily lives.Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields.Various subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for Sentiment analysis.The rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them all.According to a recent study,81%of consumers conduct online research prior to making a purchase.But the reviews available online are too huge and numerous for human brains to process and analyze.Hence,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so on.Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market research.Therefore,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words.The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study.The performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.

关键词： Sentiment analysis hotel reviews Naive Bayes algorithm consumer opinions web 2.0 machine learning

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Multilingual sentiment Mining System to Prognosticate Governance

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Computers, Materials & Continua 2022年第4期71卷 389-406页

作者： Muhammad Shahid Bhatti Saman Azhar Abid Sohail Mohammad Hijji Hamna Ayemen Areesha Ramzan COMSATS University Islamabad Lahore CampusLahorePakistan Computer Science Department University of TabukTabukSaudi Arabia

In the age of the internet,social media are connecting us all at the tip of our fingers.People are linkedthrough different social media.The social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights.This paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman Urdu.Explore machine learning solutions for Sentiment analysis and train models,collect the data on government from Twitter,apply Sentiment analysis,and provide a python library that classifies text sentiment.Training data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:11k.Five different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is explored.The Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%accuracy.Lastly,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy.

关键词： Multilingual NLP artificial intelligence government Sentiment analysis NLP NLTK ensemble technique multilingual twitter data science

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Deep Learning and Machine Learning-Based Model for Conversational sentiment Classification

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Computers, Materials & Continua 2022年第8期72卷 2323-2339页

作者： Sami Ullah Muhammad Ramzan Talib Toqir A.Rana Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College University Faisalabad38000Pakistan Department of Computer Science and IT The University of Lahore54590Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)11800PenangMalaysia Department of Software Engineering Government College University Faisalabad38000Pakistan

In the current era of the internet,people use online media for conversation,discussion,chatting,and other similar purposes.analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks.There are several approaches to identify users’emotions fromthe conversational text for the English language,however regional or low resource languages have been neglected.The Urdu language is one of them and despite being used by millions of users across the globe,with the best of our knowledge there exists no work on dialogue analysis in the Urdu language.Therefore,in this paper,we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users’emotions from the text.To accomplish this task,we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis.After that,we have preprocessed the data and selected dialogues with common emotions.Once the dataset is prepared,we have used different deep learning and machine learning techniques for the classification of emotion.We have tuned the algorithms according to the Urdu language datasets.The experimental evaluation has shown encouraging results with 67%accuracy for the Urdu dialogue datasets,more than 10,000 dialogues are classified into five emotions i.e.,joy,fear,anger,sadness,and neutral.We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

关键词： Dialogue analysis conversational opinion mining Sentiment analysis Sentiment analysis in Urdu language deep learning machine learning

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AI-based Automated Extraction of Location-Oriented COVID-19 sentiments

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Computers, Materials & Continua 2022年第8期72卷 3631-3649页

作者： Fahim K.Sufi Musleh Alsulami Federal Government MelbourneVIC3000Australia Information Systems Department Umm Al-Qura University(UQU)Makkah24382Saudi Arabia

The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic decision-makers.To this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 situation.We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,Sentiment analysis,location entity detection,and decomposition tree analysis.We deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,2022.The system was evaluated on a limited dataset between July 15,2021 to August 10,2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,respectively.As of 12 January,2022,the proposed solution has detected 43,169 locations using entity recognition.According to the best of our knowledge,this study is the first to report location intelligence with entity detection,Sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.

关键词： Entity recognition AI-based social media monitoring Sentiment analysis decision support system COVID-19

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Syntax-Based Aspect sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

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Computers, Materials & Continua 2023年第5期75卷 2873-2888页

作者： Zhaoliang Wu Shanyu Tang Xiaoli Feng Jiajun Zou Fulian Yin College of Information and Communication Engineering Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China Cybersecurity and Criminology Centre University of West LondonSt.Mary’s RoadLondon W55RFLondonUK Department of Electronic Engineering Tsinghua UniversityShuangqing Road No.30Beijing100084China The State Key Laboratory of Media Convergence and Communication Communication University of ChinaDingfuzhuang East Street No.1Beijing100024China

Aspect-Based Sentiment analysis(ABSA)is one of the essential research in the field of Natural Language Processing(NLP),of which Aspect sentiment Quad Prediction(ASQP)is a novel and complete subtask.ASQP aims to accurately recognize the sentiment quad in the target sentence,which includes the aspect term,the aspect category,the corresponding opinion term,and the sentiment polarity of opinion.Nevertheless,existing approaches lack knowledge of the sentence’s syntax,so despite recent innovations in ASQP,it is poor for complex cyber comment processing.Also,most research has focused on processing English text,and ASQP for Chinese text is almost non-existent.Chinese usage is more casual than English,and individual characters contain more information.We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies.In this framework,part of speech(POS)and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure.Besides,we design a relation extraction module,which provides a bridge for the overall extraction of the framework.A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1.Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.

关键词： ABSA ASQP Sentiment analysis Chinese comments

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A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

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Computers, Materials & Continua 2023年第10期77卷 115-137页

作者： Bishrul Haq Sher Muhammad Daudpota Ali Shariq Imran Zenun Kastrati Waheed Noor Department of Computer Science Sukkur IBA UniversitySukkur65200Pakistan Department of Computer Science Norwegian University of Science and Technology(NTNU)Gjøvik2815Norway Department of Informatics Linnaeus UniversityVäxjö35195Sweden Department of Computer Science&IT University of BalochistanQuetta87300Pakistan

The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated aspects.It is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally important.Aspect-based Sentiment analysis(ABSA)refers to aspects extracted from an opinionated text.The literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect tagging.This study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect categories.Hence,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented text.The predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect terms.The study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.

关键词： Natural language processing Sentiment analysis aspect-based Sentiment analysis topic-modeling POS tagging zero-shot learning

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