Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Braig, Niklas; Benz, Alina; Voth, Soeren; Breitenbach, Johannes; Buettner, Ricardo

doi:10.1109/access.2023.3242234

Cited by 24 publications

(6 citation statements)

References 143 publications

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“…We chose Twitter as the source of data for our approach since recent scientific reviews confirmed this medium's fitness for emotion analysis [39,40]. These reviews examined a considerable number of published articles (>40) that studied users' behavior during the COVID-19 pandemic through different perspectives, mainly utilizing the platform's data.…”

Section: Dataset Compilationmentioning

confidence: 99%

Analysis of Pharmaceutical Companies’ Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

Gyftopoulos,

Drosatos,

Fico

et al. 2024

Behavioral Sciences

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The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an “infodemic”, a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public’s response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public’s preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

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Section: Dataset Compilationmentioning

confidence: 99%

Analysis of Pharmaceutical Companies’ Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

Gyftopoulos,

Drosatos,

Fico

et al. 2024

Behavioral Sciences

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“…Braig et al [ 49 ] studied the human behaviours during the pandemic of COVID-19. Authers mapped the people thinking, feeling, and acting on a sentiment dashboard.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Ali,

Masood

2024

PLoS ONE

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Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

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“…The report advises that scientists fine-tune the hyperparameters of boosting type models and broaden the methodologies used to improve the strategy. N. Braig et al [20] analyze sentiment analysis literature using COVID-19…”

Section: Literature Reviewmentioning

confidence: 99%

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

Islam,

Sheakh,

Sadik

et al. 2024

JCC

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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 F 1 -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.

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Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Cited by 24 publications

References 143 publications

Analysis of Pharmaceutical Companies’ Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

Analysis of Pharmaceutical Companies’ Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public

Evaluation of adjective and adverb types for effective Twitter sentiment classification

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

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