Migrants vs. stayers in the pandemic – A sentiment analysis of Twitter content

Czeranowska, Olga; Chlasta, Karol; Miłkowski, Piotr; Grabowska, Izabela; Kocoń, Jan; Hwaszcz, Krzysztof; Wieczorek, Jan; Jastrzębowska, Agata

doi:10.1016/j.teler.2023.100059

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…While machine learning has been widely used for sentiment analysis, there is an urgent need for an advanced approach [4]. Sentiment analysis is suitable to see how the public, especially Twitter users, responds to the policy [3], [5].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Al Habesyah,

Herteno,

Indriani

et al. 2024

j.electron.electromedical.eng.med.inform

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TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Al Habesyah,

Herteno,

Indriani

et al. 2024

j.electron.electromedical.eng.med.inform

View full text Add to dashboard Cite

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Exploring the perception of ethnic migrants in Kolkata, India: A comparative study using sentiment analysis

Samanta,

Chattopadhyay

2024

Journal of Urban Affairs

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Feature Selection and Performance Evaluation of Buzzer Classification Model

Afra,

Fajri,

Prafitia

et al. 2024

J. Optimasi Sist. Ind.

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In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.

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Migrants vs. stayers in the pandemic – A sentiment analysis of Twitter content

Cited by 3 publications

References 69 publications

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning

Exploring the perception of ethnic migrants in Kolkata, India: A comparative study using sentiment analysis

Feature Selection and Performance Evaluation of Buzzer Classification Model

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