Sentiment analysis of COVID-19 cases in Greece using Twitter data

Samaras, Loukas; García‐Barriocanal, Elena; Sicilia, Miguel–Ángel

doi:10.1016/j.eswa.2023.120577

Cited by 19 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since the behavior of social media users also varies with language [41], having datasets in various languages besides English is crucial. Therefore, efforts have been made to compile multilingual corpora [42,43] as well as language-specific datasets such as Portuguese [44,45], Arabic [46,47], French [48], among others [49][50][51]. For the Spanish language, there are annotated tweet datasets for tasks such as hate speech detection [52], aggression detection [53], LGBT-phobia detection [54], and automatic stance detection [55], among others.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

View full text Add to dashboard Cite

This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

View full text Add to dashboard Cite

show abstract

“…In study [58], the spiritual factor of religious faith is analyzed, which conveyed to the population words such as peace, trust, hope for healing, explaining the positive evaluation of feelings. A number of studies have analyzed the feelings conveyed by the opinions of all categories of the population during the COVID-19 pandemic on social networks in Greece or China [59,60] or in epidemics, pandemics, viruses, or outbreaks in the last 10 years [61]. The content analysis of natural language text uses terms such as word dictionary, word cloud, word frequency.…”

Section: Literature Reviewmentioning

confidence: 99%

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Pirnau,

Botezatu,

Priescu

et al. 2024

Electronics

View full text Add to dashboard Cite

Researchers from different fields have studied the effects of the COVID-19 pandemic and published their results in peer-reviewed journals indexed in international databases such as Web of Science (WoS), Scopus, PubMed. Focusing on efficient methods for navigating the extensive literature on COVID-19 pandemic research, our study conducts a content analysis of the top 1000 cited papers in WoS that delve into the subject by using elements of natural language processing (NLP). Knowing that in WoS, a scientific paper is described by the group Paper = {Abstract, Keyword, Title}; we obtained via NLP methods the word dictionaries with their frequencies of use and the word cloud for the 100 most used words, and we investigated if there is a degree of similarity between the titles of the papers and their abstracts, respectively. Using the Python packages NLTK, TextBlob, VADER, we computed sentiment scores for paper titles and abstracts, analyzed the results, and then, using Azure Machine Learning-Sentiment analysis, extended the range of comparison of sentiment scores. Our proposed analysis method can be applied to any research topic or theme from papers, articles, or projects in various fields of specialization to create a minimal dictionary of terms based on frequency of use, with visual representation by word cloud. Complementing the content analysis in our research with sentiment and similarity analysis highlights the different or similar treatment of the topics addressed in the research, as well as the opinions and feelings conveyed by the authors in relation to the researched issue.

show abstract

“…Using the Paul Ekman classification, they found that the most frequent emotions were surprise at the emerging contagion and anger over the imposed isolation, leading to a "fear versus anger" response. Samaras, García-Barriocanal and Sicilia [13] focused on the second wave, and they aimed at evaluating the accuracy of existing sentiment and emotion lexicons. They found a diminishing interest in tweeting about COVID-19, a lower positive polarity than in other countries, while the dominant emotions were surprise, disgust and anger.…”

Section: Literature Reviewmentioning

confidence: 99%

Emotions during the Pandemic’s First Wave: The Case of Greek Tweets

Skarpelos,

Messini,

Roinioti

et al. 2024

Digital

View full text Add to dashboard Cite

While most published research on COVID-19 focused on a few countries and especially on the second wave of the pandemic and the vaccination period, we turn to the first wave (March–May 2020) to examine the sentiments and emotions expressed by Twitter users in Greece. Using deep-learning techniques, the analysis reveals a complex interplay of surprise, anger, fear, and sadness. Initially, surprise was dominant, reflecting the shock and uncertainty accompanying the sudden onset of the pandemic. Anger replaced surprise as individuals struggled with isolation and social distancing. Despite these challenges, positive sentiments of hope, resilience and solidarity were also expressed. The COVID-19 pandemic had a strong imprint upon the emotional landscape worldwide and in Greece. This calls for appealing to emotions as well as to reason when crafting effective public health strategies.

show abstract

Sentiment analysis of COVID-19 cases in Greece using Twitter data

Cited by 19 publications

References 34 publications

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Content Analysis Using Specific Natural Language Processing Methods for Big Data

Emotions during the Pandemic’s First Wave: The Case of Greek Tweets

Contact Info

Product

Resources

About