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Multi-label Emotion classification of COVID–19 Tweets with Deep Learning and Topic Modelling

Computer Systems Science & Engineering 2023年第6期45卷 3005-3021页

作者： K.Anuratha M.Parvathy Department of Information Technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia Department of Computer Science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

The COVID-19 pandemic has become one of the severe diseases in recent *** it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the *** public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those *** topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity *** research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 *** the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July *** extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion classification Convolution Neural Network(IBEC-CNN)model which is *** classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high *** identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been *** of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%neg

关键词： Twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare

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OKO-SVM:Online kernel optimization-based support vector machine for the incremental learning and classification of the sentiments in the train reviews

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第6期9卷 100-126页

作者： Rashmi K.Thakur Manojkumar V.Deshpande MPSTME NMIMS UniversityMumbaiIndia Prestige Institute of Engineering Management&Research Indore India

Online incremental learning is one of the emerging research interests among the researchers in the recent *** sentiment classification through the online incremental learning faces many challenges due to the limitations in the memory and the computing resources available for processing the online *** work has introduced an online incremental learning algorithm for classifying the train *** sentiments available in the reviews provided for the public services are necessary for improving the quality of the *** work proposes the online kernel optimizationbased support vector machine(OKO-SVM)classifier for the sentiment classification of the train *** paper is the extension of the previous work kernel optimizationbased support vector machine(KO-SVM).The OKO-SVM classifier uses the proposed fuzzy bound for modifying the weight for each incoming review database for the particular time *** simulation uses the standard train review and the movie review database for the *** the simulation results,it is evident that the proposed model has achieved a better performance with the values of 84.42%,93.86%,and 74.56%regarding the accuracy,sensitivity,and specificity while classifying the train review database.

关键词： Online incremental learning train reviews sentiment classification kernel optimization train review database.

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sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

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Computer Systems Science & Engineering 2023年第6期45卷 3231-3246页

作者： E.Susi A.P.Shanthi Department of Computer Science&Engineering Anna UniversityChennai600025Tamil NaduIndia

Handling sentiment drifts in real time twitter data streams are a challen-ging task while performing sentiment classifications,because of the changes that occur in the sentiments of twitter users,with respect to *** growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real *** work proposes an adap-tive learning algorithm-based framework,Twitter sentiment Drift Analysis-Bidir-ectional Encoder Representations from Transformers(TSDA-BERT),which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real *** framework also works on static data by converting them to data streams using the Kafka *** experiments conducted on real time and simulated tweets of sports,health care andfinancial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model,with accuracies of 91%,87%and 90%,*** the results have been provided only for a few topics,as a proof of concept,this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

关键词： sentiment drift sentiment classification big data BERT real time data streams twitter

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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An Opinion Mining Task in Turkish Language： A Model for Assigning Opinions in Turkish Blogs to the Polarities

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Journalism and Mass Communication 2013年第3期3卷 179-198页

作者： Cigdem Aytekin Marmara University Istanbul Turkey

Global changes took place at a neck-breaking speed in lots of fields along with the Web 2.0 era, which can be stated as the new Internet trend. Web pages which once were a statical structure that can be said to become dynamic pages created by users, and in this regard they can be said to have been democratized by evolving. Social media, which were structured alongside with this era, by providing a large data flow for businesses, present new and improvable opportunities in the field of creating effective strategies. There are lots of blogs in today＇s Internet environment which includes customer ideas regarding the products/services that they possess. This environment, which in a way globalizes the customer ideas, is a new medium suitable for examination in terms of its increasing the business-customer interaction and due to its transporter nature; it provides the text data that may be analyzed in the field of Customer Relationship Management to businesses. Thus, businesses should follow blog environments to see how the product/service they provide is greeted in terms of the customer focus and it should be seen as an important job on which they can conduct effective analyses. For this purpose, a model proposal that will assign the ideas to the Turkish blogs was given in the study. Opinion mining methods were used in the model, and so to perceive a general look-on about products/services, a methodology was devised, which will assign the text based opinion data on the Turkish blogs to the poles. Success of the pole assignment of the model is evaluated with the precision measure.

关键词： opinion mining text classification sentiment classification semantic orientation positive/negative polarity

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Web文本情感分类研究综述

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图书馆学情报学 2011年第1期

作者：王洪伟刘勰尹裴廖雅国

对用户发表在Web上的评论进行分析，能够识别出隐含在其中的情感信息，并发现用户情感的演变规律。为此，本文对Web文本情感分类的研究进行综述。将情感分类划分为三类任务：主客观分类、极性判别和强度判别，对各自的研究进展进行总结... 详细信息

对用户发表在Web上的评论进行分析，能够识别出隐含在其中的情感信息，并发现用户情感的演变规律。为此，本文对Web文本情感分类的研究进行综述。将情感分类划分为三类任务：主客观分类、极性判别和强度判别，对各自的研究进展进行总结。其中将情感极性判别的方法分为基于情感词汇语义特性的识别和基于统计自然语言处理的识别方法。分析了情感分类中的语料库选择和研究难点。最后总结了情感分类的应用现状，并指出今后的研究方向。

关键词： Web文本情感分类综述主观性文本 Web texts sentiment classification Survey Subjective text

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自动情感文本分类研究综述

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图书馆学情报学 2011年第9期

作者：夏火松彭柳艳余梦麟

情感分类及其应用是目前研究的一个热点，是自然语言处理，机器学习和心理学等多学科交叉的研究课题，在很多领域都有实际的应用，如产品的声誉分析，舆情跟踪，博客兴趣分析等。论文对情感分类目前国内外的研究概貌进行了分析，将现有... 详细信息

情感分类及其应用是目前研究的一个热点，是自然语言处理，机器学习和心理学等多学科交叉的研究课题，在很多领域都有实际的应用，如产品的声誉分析，舆情跟踪，博客兴趣分析等。论文对情感分类目前国内外的研究概貌进行了分析，将现有文献中的研究方向分为四个类别，并对这四个类别分别进行了描述，对情感分类中的关键问题进行了研究，提出了情感分类的一般框架，最后对目前研究中存在的不足进行了讨论，对情感分类研究的发展方向进行了展望。

关键词：情感语义词典主观识别情感分类舆情跟踪声誉分析研究综述 sentiment semantic lexicon Subjectivity identification sentiment classification Public opinion track Reputation analysis Review

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