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Theocharopoulos PC, Tsoukala A, Georgakopoulos SV, Tasoulis SK, Plagianakos VP. Analysing sentiment change detection of Covid-19 tweets. Neural Comput Appl 2023; 35:1-11. [PMID: 37362564 PMCID: PMC10230484 DOI: 10.1007/s00521-023-08662-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.
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Affiliation(s)
| | - Anastasia Tsoukala
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Sotiris K. Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
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Barmpas P, Tasoulis S, Vrahatis AG, Georgakopoulos SV, Anagnostou P, Prina M, Ayuso-Mateos JL, Bickenbach J, Bayes I, Bobak M, Caballero FF, Chatterji S, Egea-Cortés L, García-Esquinas E, Leonardi M, Koskinen S, Koupil I, Paja̧k A, Prince M, Sanderson W, Scherbov S, Tamosiunas A, Galas A, Haro JM, Sanchez-Niubo A, Plagianakos VP, Panagiotakos D. A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Inf Sci Syst 2022; 10:6. [PMID: 35529251 PMCID: PMC9013733 DOI: 10.1007/s13755-022-00171-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
The ATHLOS cohort is composed of several harmonized datasets of international groups related to health and aging. As a result, the Healthy Aging index has been constructed based on a selection of variables from 16 individual studies. In this paper, we consider additional variables found in ATHLOS and investigate their utilization for predicting the Healthy Aging index. For this purpose, motivated by the volume and diversity of the dataset, we focus our attention upon data clustering, where unsupervised learning is utilized to enhance prediction power. Thus we show the predictive utility of exploiting hidden data structures. In addition, we demonstrate that imposed computation bottlenecks can be surpassed when using appropriate hierarchical clustering, within a clustering for ensemble classification scheme, while retaining prediction benefits. We propose a complete methodology that is evaluated against baseline methods and the original concept. The results are very encouraging suggesting further developments in this direction along with applications in tasks with similar characteristics. A straightforward open source implementation for the R project is also provided (https://github.com/Petros-Barmpas/HCEP). Supplementary Information The online version contains supplementary material available at 10.1007/s13755-022-00171-1.
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Affiliation(s)
- Petros Barmpas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Sotiris Tasoulis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Aristidis G. Vrahatis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Panagiotis Anagnostou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Matthew Prina
- Social Epidemiology Research Group. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Global Health Institute, King’s College London, London, UK
| | - José Luis Ayuso-Mateos
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Psychiatry, Universidad Autónoma de Madrid, Madrid, Spain
- Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa (IIS Princesa), Madrid, Spain
| | - Jerome Bickenbach
- Swiss Paraplegic Research, Guido A. Zäch Institute (GZI), Nottwil, Switzerland
- Department of Health Sciences & Health Policy, University of Lucerne, Lucerne, Switzerland
| | - Ivet Bayes
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Martin Bobak
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Francisco Félix Caballero
- Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Idipaz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain
| | - Somnath Chatterji
- Information, Evidence and Research, World Health Organization, Geneva, Switzerland
| | - Laia Egea-Cortés
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Esther García-Esquinas
- Department Preventive Medicine and Public Health, Universidad Autónoma de Madrid, Idipaz, Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, CIBERESP, Madrid, Spain
| | | | - Seppo Koskinen
- National Institute for Health and Welfare (THL), Helsinki, Finland
| | - Ilona Koupil
- Centre for Health Equity Studies, Department of Public Health Sciences, Stockholm University, Stockholm, Sweden
- Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden
| | - Andrzej Paja̧k
- Department of Epidemiology and Population Studies, Jagienllonian University, Krakow, Poland
| | - Martin Prince
- Global Health Institute, King’s College London, London, UK
- Centre for Global Mental Health. Health Service and Population Research Department, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Warren Sanderson
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Department of Economics, Stony Brook University, Stony Brook, NY USA
| | - Sergei Scherbov
- International Institute for Applied Systems Analysis, World Population Program, Wittgenstein Centre for Demography and Global Human Capital, Laxenburg, Austria
- Austrian Academy of Science, Vienna Institute of Demography, Vienna, Austria
- Russian Presidential Academy of National Economy and Public Administration (RANEPA), Moscow, Russian Federation
| | | | - Aleksander Galas
- Department of Epidemiology and Preventive Medicine, Jagiellonian University, Krakow, Poland
| | - Josep Maria Haro
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Albert Sanchez-Niubo
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Research, Innovation and Teaching Unit. Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain
| | - Vassilis P. Plagianakos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | - Demosthenes Panagiotakos
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece
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Iakovidis DK, Georgakopoulos SV, Vasilakakis M, Koulaouzidis A, Plagianakos VP. Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification. IEEE Trans Med Imaging 2018; 37:2196-2210. [PMID: 29994763 DOI: 10.1109/tmi.2018.2837002] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.
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