From Text to Understanding

Descripción

Social media and other sources of text data are still underutilized resources in public decision-making. Most public organizations and governmental bodies rely mainly only surveys, interviews and other traditional methods for gathering opinions. One issues is a lack of easy-to-use tools for mass text data processing that would enable these organizations to process and understand this type of data. This book introduces a novel text data analysis framework designed for public decision making, specifically on the level of municipalities. The framework combines sentiment analysis with topic modelling and a fuzzy-based approach for capturing the diversity in sentiment arising from the fact that different people have different opinions on a given topic. The book is recommended for practitioners in public decision making as well as researchers analyzing large amounts of text data in order to understand people’s opinions.

Información del libro

Editorial

Springer Nature

Fecha de publicación

2025-09-26

Páginas

123

Idioma

EN

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