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Topic Modelling and sentimental analysis of Students’Reviews

Computers, Materials & Continua 2023年第3期74卷 6835-6848页

作者： Omer S.Alkhnbashi Rasheed Mohammad Nassr Information and Computer Science Department King Fahd University of Petroleum and Minerals31261Kingdom Saudi Arabia Computer Science Department School of Computing and Digital TechnologyBirmingham City UniversityBirminghamB55JUUnited Kingdom

Globally,educational institutions have reported a dramatic shift to online learning in an effort to contain the COVID-19 *** fundamental concern has been the continuance of *** a result,several novel solutions have been developed to address technical and pedagogical ***,these were not the only difficulties that students *** implemented solutions involved the operation of the educational process with less regard for students’changing circumstances,which obliged them to study from *** should be asked to provide a full list of their *** a result,student reflections,including those from Saudi Arabia,have been analysed to identify obstacles encountered during the COVID-19 ***,most of the analyses relied on closed-ended questions,which limited student *** delve into students’responses,this study used open-ended questions,a qualitative method(content analysis),a quantitative method(topic modelling),and a sentimental *** study also looked at students’emotional states during and after the COVID-19 *** terms of determining trends in students’input,the results showed that quantitative and qualitative methods produced similar *** had unfavourable sentiments about studying during COVID-19 and positive sentiments about the face-to-face ***,topic modelling has revealed that the majority of difficulties are more related to the environment(home)and social *** were less accepting of online *** a result,it is possible to conclude that face-to-face study still attracts students and provides benefits that online study cannot,such as social interaction and effective eye-to-eye communication.

关键词： Topic modelling sentimental analysis COVID-19 students’input

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Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

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Computer Systems Science & Engineering 2022年第2期40卷 735-747页

作者： K.Sangeetha D.Prabha Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning *** this,most research uses sentiment analysis to track students’*** sentence-level sentiment analysis focuses on the whole sentence *** studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a *** is a need to extract the targets in a given sentence which helps to find the sentiment towards those *** extraction is the subtask of targeted sentiment *** this paper,we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields(CRF).A Parallel fusion neural network model is designed to perform this *** evaluate the model using the Michigan dataset and we build a dataset for target extraction from student *** experimental results show that our proposed fusion model achieves better results compared to baseline models.

关键词： Feedback sentimental analysis deeplearning integrated CRF

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Artificial Intelligence Based Sentence Level Sentiment analysis of COVID-19

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Computer Systems Science & Engineering 2023年第10期47卷 791-807页

作者： Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud Department of Software Engineering University of Management and TechnologyLahore54770Pakistan School of Computer Science The University of SydneySydneyAustralia Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Computer Science Umm Al Qura UniversityMecca24211Saudi Arabia Computer Science Department Faculty of Computers and InformationKafrelsheikh UniversityKafrelsheikhEgypt

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern ***,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment *** recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or *** or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing *** process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning *** deep learning-based model long-short-term memory(LSTM)performed better than machine learning *** short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.

关键词： COVID-19 artificial intelligence machine learning deep learning sentimental analysis support vector classifier

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Trend analysis of Large-Scale Twitter Data Based on Witnesses during a Hazardous Event: A Case Study on California Wildfire Evacuation

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World Journal of Engineering and Technology 2021年第2期9卷 229-239页

作者： Syed A. Morshed Khandakar Mamun Ahmed Kamar Amine Kazi Ashraf Moinuddin Department of Civil and Environmental Engineering College of Engineering and Computing Florida International University Miami Florida United States School of Computing and Information Sciences Florida International University Miami Florida United States Department of Computer Engineering University of Memphis Memphis Tennessee United States

Social media data created a paradigm shift in assessing situational awareness during a natural disaster or emergencies such as wildfire, hurricane, tropical storm etc. Twitter as an emerging data source is an effective and innovative digital platform to observe trend from social media users’ perspective who are direct or indirect witnesses of the calamitous event. This paper aims to collect and analyze twitter data related to the recent wildfire in California to perform a trend analysis by classifying firsthand and credible information from Twitter users. This work investigates tweets on the recent wildfire in California and classifies them based on witnesses into two types: 1) direct witnesses and 2) indirect witnesses. The collected and analyzed information can be useful for law enforcement agencies and humanitarian organizations for communication and verification of the situational awareness during wildfire hazards. Trend analysis is an aggregated approach that includes sentimental analysis and topic modeling performed through domain-expert manual annotation and machine learning. Trend analysis ultimately builds a fine-grained analysis to assess evacuation routes and provide valuable information to the firsthand emergency responders.

关键词： Wildfire Evacuation Twitter Large-Scale Data Topic Model sentimental analysis Trend analysis

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Aspect Based User Reviews Classification

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Instrumentation 2020年第2期7卷 9-19页

作者： Roch DANISTAN Thulasika ARUNAKIRINATHAN Archchana SIVARAJAH Yanusha MEHENDRAN Jayalath EKANAYAKE Dept.of Computer Science and Informatics Faculty of Applied SciencesUva Wellassa UniversityPassara Rd.Badulla90000Sri Lanka

At present online shopping is very popular as it is very convenient for the ***,selecting smartphones from online shops is bit difficult only from the pictures and a short description about the item,and hence,the customers refer user reviews and star *** user reviews are represented in human languages,sometimes the real semantic of the reviews and satisfaction of the customers are different than what the star rating ***,reading all the reviews are not possible as typically,a smartphone gets thousands of reviews in popular online shopping platform like ***,this work aims to develop a recommended system for smartphones based on aspects of the phones such as screen size,resolution,camera quality,battery life *** by *** that end we apply hybrid approach,which includes three lexicon-based methods and three machine learning modals to analyze specific aspects of user reviews and classify the reviews into six categories--best,better,good or somewhat for positive comments and for negative comments bad or not recommended--.The lexicon-based tool called AFINN together with Random Forest prediction model provides the best classification F1-score *** system can be customized according to the required aspects of smartphones and the classification of reviews can be done accordingly.

关键词： User-reviews Classification Aspects of Smartphones Reviews of Smart Phones Classification Algorithms Lexicon-based Methods sentimental analysis

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Naïve Bayes Algorithm for Large Scale Text Classification

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Instrumentation 2021年第4期8卷 55-62页

作者： Pirunthavi SIVAKUMAR Jayalath EKANAYAKE Department of Information and Communication Technology Faculty of TechnologyRajarata University of Sri LankaMihintale 50300Sri Lanka Department of Computer Science and Informatics Faculty of Applied SciencesUva Wellassa University of Sri LankaBadulla 90000Sri Lanka

This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in *** contains large unstructured and unorganized comments and reactions,which carry important *** large amounts of data and extracting useful information is a challenging *** extracted information can be considered as new knowledge and can be used for deci *** extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved *** method provided a decent 80%accuracy in classifying those *** experiment shows that the proposed method provides excellent adaptability for large-scale text classification.

关键词： Naïve Bayes Text Classification YouTube sentimental analysis

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