<p>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.</p><p>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.</p><p>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.</p>

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From Text to Understanding

  • Miloš Švaňa,
  • František Zapletal,
  • Miroslav Hudec

摘要

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.