Beyond the Numbers: Illuminating Business Insights with Topic Modeling in Earnings Calls Disclaimer: The Content is for informational purposes only, you…
The media has been used to disseminate public information amid the Covid-19 pandemic. However, Covid-19 news has triggered emotional responses in people that have impacted their mental well-being and led to news avoidance. To understand the emotional response to Covid-19 news, we studied user comments on news published on Twitter by 37 media outlets in 11 countries from January 2020 to December 2022. We employed a deep-learning-based model to identify the basic human emotions defined by Ekman in comments related to Covid-19 news. Additionally, we implemented Latent Dirichlet Allocation (LDA) to identify the news topics. Our analysis found that while nearly half of the user comments showed no significant emotions, negative emotions were more common. Anger was the most prevalent emotion, particularly in the media and comments regarding political responses and governmental actions in the United States. On the other hand, joy was mainly linked to media outlets from the Philippines and news
/PRNewswire/ JMIR Publications published "User-Chatbot Conversations During the COVID-19 Pandemic: A Study Based on Topic Modeling and Sentiment Analysis".
The goal of Topic Modeling is good visual representation of a topic, and then a good visual representation of exactly for which types of products Mandatory Medical Device Reporting, addressing that topic were filed. Natural Language offers insights from large databases
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