vimarsana.com

Page 4 - Latent Dirichlet Allocation News Today : Breaking News, Live Updates & Top Stories | Vimarsana

AI and Government Agency Request for Comments or Info

Unpacking Averages: Using Natural Language Processing to Extract Quality Information from MDRs | Epstein Becker & Green

Over the spring and summer, I did a series of posts on extracting quality information from FDA enforcement initiatives like warning letters, recalls, and inspections. But obviously FDA .

MDRs: What is Natural Language Processing and Topic Modeling?

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

Mapping the Public Voice for Development—Natural Language Processing of Social Media Text Data: A Special Supplement of Key Indicators for Asia and the Pacific 2022

This publication explores how natural language processing (NLP) techniques can be applied to social media text data to map public sentiment and inform development research and policy making.

Frontiers | Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter

In recent years, a concerted political effort has been made at national and EU level to promote the consumption of healthy foods. The European Commission (EC) expressed a need for a harmonized, mandatory front-of-pack nutrition labelling (FOPL) system at EU level. The EC intends to adopt the proposal by the end of 2022. Our research work seeks to understand the public discourse on FOPL in the EU through Twitter, by content analysis of tweets, sentiment analysis and mapping network characteristics. Search and data collection of tweets were carried out by using Twitter Application programming interface (API) without time or language limit. The content was coded using the QRS Nvivo software package and analysed by themes. Automatic sentiment analysis was carried out by QSR Nvivo, network analysis by Gephi 0.9.2. 4073 tweets were posted, mainly from the United Kingdom, Spain and France. The emerging themes from Twitter discussions include food labelling types, food industry, healthy food v

© 2025 Vimarsana

vimarsana © 2020. All Rights Reserved.