Press media impact of the Cumbre Vieja volcano activity in the island of La Palma (Canary Islands): A machine learning and sentiment analysis of the news published during the volcanic eruption of 2021

Piña, J. Urias; Mas, F. Magdaleno; Lahoz-Beltrá, Rafael

doi:10.1016/j.ijdrr.2023.103694

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…Public opinion has become a crucial source in an individual's decision-making process regarding a product (Edara et al, 2023;Kaur & Sharma, 2023). Furthermore, sentiment analysis is a popular field of research, as it offers benefits for various aspects, ranging from social media and public opinion (Bhargav, 2022;Keakde, 2022;Talaat, 2023;Uma, 2022), stock markets and finance (Chong, 2022), brand management and marketing (Kumar, 2022;Win, 2022), elections and political analysis (Keakde, 2022;Sutriawan, 2023;Talaat, 2023), customer service and feedback (Bharathi, 2023;Bhargav, 2022), health and medical research (Che, 2023;Chong, 2022), entertainment and film (Zheng, 2019), education (Derisma, 2020;Keakde, 2022), environmental issues and natural disasters (Behl, 2021;Navarro, 2023;Nguyen, 2023;Pappas, 2017;V. Priya, 2016;Ragini, 2018), and travel and tourism (Gholipour, 2020;Luo, 2021;P.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Defit,

Windarto,

Alkhairi

2024

telematika.

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Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Defit,

Windarto,

Alkhairi

2024

telematika.

View full text Add to dashboard Cite

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Assessment of text-generated supply chain risks considering news and social media during disruptive events

Sadeek

Hanaoka

2023

Soc. Netw. Anal. Min.

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Information flow is an important task in a supply chain network. Disruptive events often impede this flow due to confounding factors, which may not be identified immediately. The objective of this study is to assess supply chain risks by detecting significant risks, examining risk variations across different time phases and establishing risk sentiment relationships utilizing textual data. We examined two disruptive events—coronavirus disease 2019 (Omicron phase) and the Ukraine–Russia war—between November 2021 and April 2022. Data sources included news media and Twitter. The Latent Dirichlet Allocation algorithm was applied to the textual data to extract potential text-generated risks in the form of “topics.” A proportion of these risks were analyzed to assess their time-varying nature. Natural language processing-based sentiment analysis was applied to these risks to infer the sentiment coming from the media using the ordered probit model. The results identify various unnoticed risks, for example: logistics tension, supply chain resiliency, ripple effect, regional supply chain, etc. that may adversely affect supply chain operations if not considered. The outcomes also indicate that textual data sources are capable of capturing risks before the events actually occur. The outcomes further suggest that text data could be valuable for strategic decision making and improving supply chain visibility.

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The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things

Wang,

Liu,

Shu

2024

IEEE Access

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Press media impact of the Cumbre Vieja volcano activity in the island of La Palma (Canary Islands): A machine learning and sentiment analysis of the news published during the volcanic eruption of 2021

Cited by 6 publications

References 38 publications

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Assessment of text-generated supply chain risks considering news and social media during disruptive events

The Analysis of Communication Strategy of Disabled Sports Information Based on Deep Learning and the Internet of Things

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