55,99 €
From Valence to Emotions: How Coarse versus Fine-Grained Online Sentiment can predict Real-World Outcomes
From Valence to Emotions: How Coarse versus Fine-Grained Online Sentiment can predict Real-World Outcomes
  • Išparduota
From Valence to Emotions: How Coarse versus Fine-Grained Online Sentiment can predict Real-World Outcomes
From Valence to Emotions: How Coarse versus Fine-Grained Online Sentiment can predict Real-World Outcomes
El. knyga: 55,99 €
The growing number of user-generated content that can be found online has led to a huge amount of data that can be used for scientific research. This book investigates the prediction of certain human-related events using valences and emotions expressed in user-generated content with regard to past and current research. First, the theoretical framework of user-generated content and sentiment detection- and classification methods is explained, before empirical literature is categorized into three…
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  • Autorius: Robert Kohtes
  • Leidėjas:
  • Metai: 2014
  • Puslapiai: 78
  • ISBN-10: 3954896451
  • ISBN-13: 9783954896455
  • Formatas: PDF
  • Kalba: Anglų

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The growing number of user-generated content that can be found online has led to a huge amount of data that can be used for scientific research. This book investigates the prediction of certain human-related events using valences and emotions expressed in user-generated content with regard to past and current research. First, the theoretical framework of user-generated content and sentiment detection- and classification methods is explained, before empirical literature is categorized into three specific prediction subjects. This is followed by a comprehensive analysis including a comparison of prediction methods, consistency, and limitations with respect to each of the three predictive sources.

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55,99 €El. knyga

The growing number of user-generated content that can be found online has led to a huge amount of data that can be used for scientific research. This book investigates the prediction of certain human-related events using valences and emotions expressed in user-generated content with regard to past and current research. First, the theoretical framework of user-generated content and sentiment detection- and classification methods is explained, before empirical literature is categorized into three specific prediction subjects. This is followed by a comprehensive analysis including a comparison of prediction methods, consistency, and limitations with respect to each of the three predictive sources.

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