Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis

Nguyen, Angie; Pellerin, Robert; Lamouri, Samir; Lekens, Béranger

doi:10.1080/00207543.2022.2070044

Cited by 19 publications

(3 citation statements)

References 29 publications

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“…By automatically evaluating sentiment in large volumes of text data, businesses and organizations can acquire invaluable insights to make informed decisions and comprehend public sentiment. 54,55 AI-powered predictive models can be used to analyze historical data and predict future outbreaks. 56 These models consider a wide range of factors, such as demographics, travel patterns, weather conditions, and the genetic makeup of the pathogen, to make accurate predictions.…”

Section: Source Datamentioning

confidence: 99%

“…Sentiment analysis finds broad application in areas such as social media monitoring, brand reputation management, market research, and customer feedback analysis. By automatically evaluating sentiment in large volumes of text data, businesses and organizations can acquire invaluable insights to make informed decisions and comprehend public sentiment 54,55 …”

Section: Leveraging the Emergence Of 5g Network Technology For Advanc...mentioning

confidence: 99%

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Opportunities and challenges of 5G network technology toward precision medicine

Kang,

Lee,

Lim

et al. 2023

Clinical Translational Sci

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Moving away from traditional “one‐size‐fits‐all” treatment to precision‐based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted‐treatment. The current cutting‐edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G‐enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient‐centric care via a precision medicine approach.

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Section: Source Datamentioning

confidence: 99%

Section: Leveraging the Emergence Of 5g Network Technology For Advanc...mentioning

confidence: 99%

Opportunities and challenges of 5G network technology toward precision medicine

Kang,

Lee,

Lim

et al. 2023

Clinical Translational Sci

View full text Add to dashboard Cite

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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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