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10th Computer Science and Electronic Engineering Conference (CEEC)

作者： Wang, Lei Gan, John Q. Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781538672754

twitter is a social network that lets users post their opinions about current affairs, share their social events, and interact with others. It has now become one of the largest sources of news, with over 200 million active users monthly. This paper proposes a method to predict election results based on twitter data analysis, which analyses sentimental information using term weighting and selection to predict the popularity of candidates. The proposed method was used for predicting the result of the 2017 French presidential election. It has been shown that the proposed method significantly outperformed the Tumasjan's method and beat another method proposed by the authors in 2017 as well.

关键词： Social media analysis twitter data analysis Sentiment analysis Election prediction

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Prediction of the 2017 French Election Based on twitter data analysis 9

Prediction of the 2017 French Election Based on Twitter Data...

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9th Computer Science and Electronic Engineering (CEEC)

作者： Wang, Lei Gan, John Q. Univ Essex Sch Comp Sci & Elect Engn Colchester Essex England

ISBN: (纸本)9781538630075

twitter is a social network that lets users post their opinions about current affairs, share their social events, and interact with others. twitter has now become one of the largest sources of news, with over 200 million active users monthly. This paper proposes a method to predict election results based on twitter data analysis. The method extracts and analyses sentimental information from microblogs to predict the popularity of candidates. The proposed method was used for predicting the result of the 2017 French presidential election. It has been shown that the proposed method significantly outperformed the Tumas method, a well-recognized method for election prediction based on twitter data.

关键词： Social media analysis twitter data analysis Sentiment analysis Election prediction

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A Semi-supervised Fuzzy Co-clustering Framework and Application to twitter data analysis 4

A Semi-supervised Fuzzy Co-clustering Framework and Applicat...

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4th International Conference on Informatics, Electronics & Vision (lCIEV)

作者： Honda, Katsuhiro Ubukata, Seiki Notsu, Akira Takahashi, Norimitsu Ishikawa, Yutaka Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan Change Co Ltd Tokyo 1050001 Japan Nightley Inc Tokyo 1530042 Japan

ISBN: (纸本)9781467369022

Semi-supervised clustering is an efficient scheme for utilizing data with partial class information, where unsupervised data distributions are estimated under some supports of partial supervised class information. In this paper, a novel framework for performing fuzzy co-clustering of cooccurrence information with partial supervision is proposed, which is induced by multinomial mixture concept. Co-clustering is useful for extracting objectitem pair-wise clusters from cooccurrence information and has been utilized in various applications such as document-keyword analysis and customer-products purchase history data analysis. Several experimental results including a twitter data analysis demonstrate the ability of improving the classification quality of the fuzzified co-cluster structural knowledge. Then, the proposed semi-supervised framework is expected to be a powerful tool in Big data analysis with huge volumes of data but partial supervisions only.

关键词： data analysis fuzzy set theory social networking (online) unsupervised learning twitter data analysis cooccurrence information fuzzified cocluster structural knowledge semisupervised fuzzy coclustering framework unsupervised data distributions data analysis Estimation Linear programming Mixture models Supervised learning Training twitter data analysis Semi-supervised Learning Unsupervised learning twitter Social Media Fuzzy Set Theory linear programming Mixture models Functional training Statistical analysis Frameworks data distribution

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第31期83卷 75757-75780页

作者： Cammarano, Maria Elena Guarino, Alfonso Malandrino, Delfina Zaccagnino, Rocco Univ Salerno Comp Sci Dept Via Giovanni Paolo 2132 I-84084 Fisciano Italy

The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of "never seen" shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.

关键词： Social media analysis twitter data analysis Entertainment market analysis Machine learning Prediction

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Leveraging social media for public health: NLP implementations for blood donation data analysis in Japan

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SOCIAL NETWORK analysis AND MINING 2025年第1期15卷 1-21页

作者： Espinoza, Roberto Kishimoto, Kazumasa Liu, Chang H.o. Santos, Luciano Mori, Yukiko Yamamoto, Goshiro Kuroda, Tomohiro Kyoto Univ Grad Sch Informat Yoshida HonmachiSakyo Ku Kyoto 6068317 Japan Kyoto Univ Hosp Shogoin KawarachoSakyo Ku Kyoto 6068507 Japan Fitting Cloud Inc 85-1 Mikura ChoNakagyo Ku Kyoto 6048166 Japan

Blood donation is crucial for healthcare systems, yet maintaining an adequate supply is a persistent challenge. Traditional methods to understand public sentiment and donor behavior are often limited. Social media, particularly "X" (formerly twitter), offers a promising alternative for real-time insights. This study explores the viability of using "X" data to analyze blood donation sentiment in Japan, considering the evolving perspectives of younger generations. We replicated previous study results using the Tohoku BERT model and tested a refined blood donation tweets for user classification (BDT-UC) dataset and another customized version of the model for better classification. We also compared various topic modeling methods, including latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), and BERT-based models, using two different preprocessing techniques. Finally, we integrated the classification into the Topic Modeling process, to explore the possible impact of the previous steps in such execution, for a final evaluation. Our findings indicate that although the refined dataset has an overall lower classification performance, it improved the implementation results, ensuring more balanced labeling across the data. Our refined model had a small reduction in overall precision (from 78.4% in the best evaluated model to 75.8% in the refined model). However, we improved the implementation results, ensuring more balanced labeling across the data. For topic modeling, BERT-based topic models, particularly those preprocessed with the MeCab library, achieved higher coherence and diversity scores than traditional methods. Additionally, there were significant differences when the dataset was processed following the categories of the BDT-UC study, which used specific categories related to the tweets role in blood donation. There was increased coherence and diversity for one of the categories but notably lower coherence values for the others. This study unde

关键词： Blood donation Japanese Social networking sites (SNS) twitter data analysis BERT NLP MeCab Topic modeling BERTopic

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Hybrid machine learning models to detect signs of depression

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 38819页

作者： Khan, Shakir Alqahtani, Salihah Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Riyadh 11432 Saudi Arabia

Depression is a prevalent mental illness that can only be diagnosed through self-reporting. Unfortunately, 70% of individuals do not seek medical help in the early stages of depression. With the increasing use of social media to share daily activities and emotions, it has become a valuable tool for identifying mental health issues. To this end, this paper proposes multiple hybrid machine-learning models for sentiment analysis to detect signs of depression by analyzing twitter tweets. The study utilizes unsupervised approaches for feature extraction and supervised approaches as classifiers. The proposed models are evaluated on public sentiment tweets datasets using various performance metrics. The four modules proposed in this study can be used to detect signs of depression in text data. Module 1 uses BERT for feature extraction followed by an artificial neural network as a classification module. Module 2 involves data pre-processing followed by the TF-IDF feature extraction method and logistic regression as a classification algorithm. Module 3 also includes data pre-processing followed by the TF-IDF feature extraction method but uses a linear support vector machine as the classification algorithm. Finally, Module 4 uses the Spacy package with a small library as the feature extraction method and a linear support vector machine as the classification algorithm. Model 2 had the highest accuracy of 0.994, followed closely by model 3 with an accuracy of 0.992, while model 4 had a significantly lower accuracy of 0.868. Model 1 achieved an accuracy of 0.99.

关键词： Sentiment analysis Depression detection Social network twitter data analysis

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Sigmoidal Particle Swarm Optimization for twitter Sentiment analysis

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Computers, Materials & Continua 2023年第1期74卷 897-914页

作者： Sandeep Kumar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdul Khader Jilani Saudagar Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Social media,like twitter,is a data repository,and people exchange views on global issues like the COVID-19 *** media has been shown to influence the low acceptance of *** work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to *** work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 *** paper introduces a sigmoidal particle swarm optimization(SPSO)***,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing *** proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature *** Covid-19 vaccination tweets dataset was created and used for training,validating,and *** proposed approach outperformed considered algorithms in terms of ***,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection *** shows that augmentation improves the overall accuracy of tweet *** the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

关键词： twitter data analysis sentiment analysis social media analytics swarm intelligence COVID-19 vaccine

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Beneath the hashtag: multifaceted insights into sustainable consumption and production from historical twitter data

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SUSTAINABILITY-SCIENCE PRACTICE AND POLICY 2025年第1期21卷

作者： Tosa, Cristian Tarigan, Ari K. M. Univ Stavanger Dept Safety Econ & Planning Stavanger Norway

This research contributes to the ongoing dialogue on sustainable product categorization and representation in social media. Companies and consumers are increasingly favoring eco-friendly, green, or sustainable products due to their environmentally friendly lifecycle. This study examines a dataset of more than 30,000 tweets labeled with hashtags such as #sustainableproducts, #ecoproducts, #ecofriendlyproducts, and #greenproducts. The dataset covers ten years and includes twitter data from around the world. The study employs content and sentiment analysis to reveal that conversations around sustainable and green products often emphasize environmental impact and responsible consumption, reflecting consumer preferences and business strategies. Discussions on eco-friendly and eco-products center on sustainable materials and waste reduction, highlighting consumer choices and the need for eco-conscious product design. The general sentiment expressed in these tweets is primarily positive or neutral, although negative sentiments are frequently associated with environmental concerns such as plastic pollution. The study not only highlights the significance of digital platforms in influencing public discussions on sustainability but also emphasizes the informative power of social media in shaping sustainable narratives. It suggests the need for a comprehensive strategy encompassing transparent and balanced communication, active consumer and business engagement, and collaborative efforts among stakeholders to promote sustainable consumption on digital platforms, thereby contributing to the achievement of climate neutrality. By shedding light on the complexities and nuances of online discourse, it provides a foundation for future scholarly inquiries and informs practical efforts to promote sustainable consumption and production in the digital age.

关键词： Sustainable production and consumption sustainable products twitter/X twitter data analysis sentiment analysis

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POLITICAL TWEET SENTIMENT analysis FOR PUBLIC OPINION POLLING 49

POLITICAL TWEET SENTIMENT ANALYSIS FOR PUBLIC OPINION POLLIN...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaimakamidis, Anestis Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9798350344868;9798350344851

Public opinion measurement through polling is a classical political analysis task, e.g. for predicting national and local election results. However, polls are expensive to run and their results may be biased primarily due to improper population sampling. In this paper, we propose two innovative methods for employing tweet sentiment analysis results for public opinion polling. Our first method utilizes merely the tweet sentiment analysis results outperforming multiple well-recognised methods. In addition, we introduce a novel hybrid way to estimate electorally results from both public opinion polls and tweets. This method enables more accurate, frequent, and inexpensive public opinion estimation and is used for estimating the result of the 2023 Greek national election. Our method demonstrated lower deviation from the actual election's results than the conventional public opinion polls, introducing new possibilities for public opinion estimation using social media platforms.

关键词： twitter data analysis Sentiment analysis Popularity score Election prediction Political tweet analysis

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HCovBi-Caps: Hate Speech Detection Using Convolutional and Bi-Directional Gated Recurrent Unit With Capsule Network

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IEEE ACCESS 2022年 10卷 7881-7894页

作者： Khan, Shakir Kamal, Ashraf Fazil, Mohd Alshara, Mohammed Ali Sejwal, Vineet Kumar Alotaibi, Reemiah Muneer Baig, Abdul Rauf Alqahtani, Salihah Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia ACL Digital Bengaluru 560029 India Qatar Univ Dept Comp Engn Doha Qatar Jamia Millia Islamia Dept Comp Sci New Delhi 110025 India

Adversaries and anti-social elements have exploited the rapid proliferation of computing technology and online social media in the form of novel security threats, such as fake profiles, hate speech, social bots, and rumors. The hate speech problem on online social networks (OSNs) is also widespread. The existing literature has machine learning approaches for hate speech detection on OSNs. However, the effectiveness of contextual information at different orientations is understudied. This study presents a novel Convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech. The proposed model is evaluated over two twitter-based benchmark datasets - DS1(balanced) and DS2(unbalanced) with the best performance of 0.90, 0.80, and 0.84 respectively considering precision, recall, and f-score over unbalanced dataset. In terms of training and validation accuracy, the proposed model shows the best performance of 0.93 and 0.90, respectively, over the unbalanced dataset. In comparative evaluation, HCovBi-Caps demonstrates a significantly better performance than state-of-the-art approaches. In addition, HCovBi-Caps shows comparatively better performance over the unbalanced dataset. We also investigate the impact of different hyperparameters on the efficacy of HCovBi-Caps to ascertain the selection of their values. We observed that a higher value of routing iterations adversely affects the model performance, whereas a higher value of capsule dimension improves the performance.

关键词： Hate speech Social networking (online) Deep learning Blogs Context modeling Logic gates Logistics Hate speech detection twitter data analysis convolutional layer capsule network BiGRU deep learning

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