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Optimal Machine Learning Driven Sentiment Analysis on COVID-19 twitter data

Computers, Materials & Continua 2023年第4期75卷 81-97页

作者： Bahjat Fakieh Abdullah S.AL-Malaise AL-Ghamdi Farrukh Saleem Mahmoud Ragab Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department HECI SchoolDar Alhekma UniversityJeddahSaudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia

The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.

关键词： Sentiment analysis twitter data data mining COVID-19 machine learning artificial bee colony

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Modeling of Optimal Deep Learning Based Flood Forecasting Model Using twitter data

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Intelligent Automation & Soft Computing 2023年第2期35卷 1455-1470页

作者： G.Indra N.Duraipandian Department of Computer Science and Engineering RMK College of Engineering and TechnologyChennai601206India Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602106India

Aﬂood is a signiﬁcant damaging natural calamity that causes loss of life and property.Earlier work on the construction ofﬂood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byﬂoods.The massive amount of data generated by social media platforms such as twitter opens the door toﬂood analysis.Because of the real-time nature of twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue strategy.However,due to the shorter duration of Tweets,it is difﬁcult to construct a perfect prediction model for determiningﬂood.Machine learning(ML)and deep learning(DL)approaches can be used to statistically developﬂood prediction models.At the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forﬂood prediction.In this regard,this work provides an optimal deep learning-basedﬂood forecasting model with big data analytics(ODLFF-BDA)based on twitter data.The suggested ODLFF-BDA technique intends to anticipate the existence ofﬂoods using tweets in a big data setting.The ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable format.In addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from tweets.Furthermore,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict theﬂood.Finally,an Equilibrium Optimizer(EO)is used toﬁne-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction performance.The memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm techniques.The ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and theﬁndings showed that it outperformed other recent approaches signiﬁcantly.

关键词： Big data analytics predictive models deep learning ﬂood prediction twitter data hyperparameter tuning

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Parameter Tuned Machine Learning Based Emotion Recognition on Arabic twitter data

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Computer Systems Science & Engineering 2023年第9期46卷 3423-3438页

作者： Ibrahim M.Alwayle Badriyya B.Al-onazi Jaber S.Alzahrani Khaled M.Alalayah Khadija M.Alaidarous Ibrahim Abdulrab Ahmed Mahmoud Othman Abdelwahed Motwakel Department of Computer Science College of Science and ArtsSharurahNajran UniversityNajran55461Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityNajran24211Saudi Arabia Computer Department Applied CollegeNajran UniversityNajran66462Saudi 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

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

关键词： Arabic language twitter data machine learning teaching and learning-based optimization sentiment analysis emotion classification

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Deep Learning for Depression Detection Using twitter data

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Intelligent Automation & Soft Computing 2023年第5期36卷 1301-1313页

作者： Doaa Sami Khafaga Maheshwari Auvdaiappan KDeepa Mohamed Abouhawwash Faten Khalid Karim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their end-users.It is necessary to know people’s emotions are very important to identify depressed people from their messages.Early depression detection helps to save people’s lives and other dangerous mental diseases.There are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such cases.Several machine learning methods help to identify depressed people.But the accuracy of existing methods was not satisfactory.To overcome this issue,the deep learning method is used in the proposed method for depression detection.In this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression data.Initially,the twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and lemmatization.The Adaptive Particle and grey Wolf optimization methods are used for feature selection.The MDHAN classiﬁes the twitter data and predicts the depressed and non-depressed users.Finally,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and MDHAN.The suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive rate.The experimental result shows that the proposed method achieves better accuracy,precision,recall,and F1-measure.It also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English twitter data

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Computer Systems Science & Engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English twitter data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification twitter data sentiment analysis english corpus

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Sentiment Analysis on twitter data Using Term Frequency-Inverse Document Frequency

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Journal of Computer and Communications 2022年第8期10卷 117-128页

作者： Akash Addiga Sikha Bagui Department of Computer Science University of West Florida Pensacola FL USA

This study is an exploratory analysis of applying natural language processing techniques such as Term Frequency-Inverse Document Frequency and Sentiment Analysis on twitter data. The uniqueness of this work is established by determining the overall sentiment of a politician’s tweets based on TF-IDF values of terms used in their published tweets. By calculating the TF-IDF value of terms from the corpus, this work displays the correlation between TF-IDF score and polarity. The results of this work show that calculating the TF-IDF score of the corpus allows for a more accurate representation of the overall polarity since terms are given a weight based on their uniqueness and relevance rather than just the frequency at which they appear in the corpus.

关键词： Sentiment Analysis twitter data Term Frequency Inverse Term Frequency Term Frequency-Inverse Document Frequency (TF-IDF) Social Media

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Assessing the impact of network factors and twitter data on Ethereum's popularity

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Blockchain(Research and Applications) 2023年第3期4卷 123-131页

作者： Sarah Bouraga Namur Digital Institute(NADI) University of NamurBelgium

In March 2021,we witnessed a surge in Bitcoin price.The cause seemed to be a tweet by Elon Musk.Are other blockchains as sensitive to social media as Bitcoin?And more precisely,could Ethereum's popularity be explained using social media data?This work aims to explore the determinants of Ethereum's popularity.We use both data from Etherscan to retrieve the relevant historic Ethereum factors and twitter data.Our sample consists of data ranging from 2015 to 2022.We use Ordinary Least Squares to assess the relationship between these factors(Ethereum characteristics and twitter data)and Ethereum's popularity.Our findings show that Ethereum's popularity—translated here by the number of daily new addresses—is related to the following elements:the Ether(ETH)price,the transaction fees,and the polarity of tweets related to Ethereum.The results could have multiple practical implications for both researchers and practitioners.First of all,we believe that it will enable readers to better understand the technology of Ethereum and its stake.Secondly,it will help the community identify pointers for anticipating or explaining the popularity of existing or future platforms.And finally,the results could help in understanding the factors facilitating the design of future platforms.

关键词： Blockchain technology Ethereum twitter data Platform's popularity

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Lexicon and Deep Learning-Based Approaches in Sentiment Analysis on Short Texts

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Journal of Computer and Communications 2024年第1期12卷 11-34页

作者： Taminul Islam Md. Alif Sheakh Md. Rezwane Sadik Mst. Sazia Tahosin Md. Musfiqur Rahman Foysal Jannatul Ferdush Mahbuba Begum Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science Southern Illinois University Carbondale IL USA Department of Economics & Decision Sciences University of South Dakota Vermillion SD USA Department of Computer Science and Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh

Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing of posts provides insights into someone’s current emotions. In artificial intelligence (AI) and deep learning (DL), researchers emphasize opinion mining and analysis of sentiment, particularly on social media platforms such as twitter (currently known as X), which has a global user base. This research work revolves explicitly around a comparison between two popular approaches: Lexicon-based and Deep learning-based Approaches. To conduct this study, this study has used a twitter dataset called sentiment140, which contains over 1.5 million data points. The primary focus was the Long Short-Term Memory (LSTM) deep learning sequence model. In the beginning, we used particular techniques to preprocess the data. The dataset is divided into training and test data. We evaluated the performance of our model using the test data. Simultaneously, we have applied the lexicon-based approach to the same test data and recorded the outputs. Finally, we compared the two approaches by creating confusion matrices based on their respective outputs. This allows us to assess their precision, recall, and F1-Score, enabling us to determine which approach yields better accuracy. This research achieved 98% model accuracy for deep learning algorithms and 95% model accuracy for the lexicon-based approach.

关键词： Opinion Mining Lexicon Analysis twitter data LSTM Machine Learning

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Temporal pattern mining from user-generated content

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Digital Communications and Networks 2022年第6期8卷 1027-1039页

作者： Adnan Ali Jinlong Li Huanhuan Chen Ali Kashif Bashir School of Computer Science and Technology University of Science and Technology of ChinaHefei230026AnhuiChina Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterM156BHUnited Kingdom Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Woxsen School of Business Woxsen UniversityIndia

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. dataset and codes are publicly available;the link is given in the dataset section.

关键词： Social media analysis Collaborative computing Social data twitter data Temporal patterns mining Dynamic graphs

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Disaster Impacts Surveillance from Social Media with Topic Modeling and Feature Extraction: Case of Hurricane Harvey

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International Journal of Disaster Risk Science 2022年第5期13卷 729-742页

作者： Volodymyr V.Mihunov Navid H.Jafari Kejin Wang Nina S.N.Lam Dylan Govender Department of Environmental Sciences Louisiana State UniversityBaton RougeLA 70803USA Department of Civil and Environmental Engineering Louisiana State UniversityBaton RougeLA 70803USA Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA

twitter can supply useful information on infrastructure impacts to the emergency managers during major disasters,but it is time consuming to filter through many irrelevant tweets.Previous studies have identified the types of messages that can be found on social media during disasters,but few solutions have been proposed to efficiently extract useful ones.We present a framework that can be applied in a timely manner to provide disaster impact information sourced from social media.The framework is tested on a well-studied and data-rich case of Hurricane Harvey.The procedures consist of filtering the raw twitter data based on keywords,location,and tweet attributes,and then applying the latent Dirichlet allocation(LDA) to separate the tweets from the disaster affected area into categories(topics) useful to emergency managers.The LDA revealed that out of 24 topics found in the data,nine were directly related to disaster impacts-for example,outages,closures,flooded roads,and damaged infrastructure.Features such as frequent hashtags,mentions,URLs,and useful images were then extracted and analyzed.The relevant tweets,along with useful images,were correlated at the county level with flood depth,distributed disaster aid(damage),and population density.Significant correlations were found between the nine relevant topics and population density but not flood depth and damage,suggesting that more research into the suitability of social media data for disaster impacts modeling is needed.The results from this study provide baseline information for such efforts in the future.

关键词： Disaster impacts Hurricane Harvey Infrastructure impacts Latent Dirichlet allocation(LDA) Social media analysis twitter data

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