A Practical Guide to Sentiment Analysis (Socio-Affective Computing). Questo articolo non è disponibile.
Cambria, Erik (Editor) / Das, Dipankar (Editor) / Bandyopadhyay, Sivaji (Editor) / Feraco, Antonio (Editor)
Lingua: inglese
Editore: Springer, 2018
- Brossura
- Nuovo

Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
Venditore AbeBooks dal 6 gennaio 2003
Condizione: Nuovo
EUR 250,48
Descrizione dell’articolo da parte del venditore
reprint edition. 196 pages. 9.25x6.10x0.46 inches. In Stock.
Codice articolo 3319856480
- Titolo
- A Practical Guide to Sentiment Analysis (Socio-Affective Computing)
- Autore
- Cambria, Erik (Editor) / Das, Dipankar (Editor) / Bandyopadhyay, Sivaji (Editor) / Feraco, Antonio (Editor)
- Editore
- Springer
- Anno di pubblicazione
- 2018
- Condizione
- Brand New
- Rilegatura
- Paperback
- Lingua
- inglese
- ISBN 10
- 3319856480
- ISBN 13
- 9783319856483
- Peso dell'articolo
- 3,55 chilogrammi
- Serie
- Libro 5 di 10: Socio-Affective Computing
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Dalla quarta di copertina
In this book, the authors propose an overview of the main issues and challenges associated with current sentiment analysis research and provide some insights on practical tools and techniques that can be exploited to both advance the state of the art in all sentiment analysis subtasks and explore new areas in the same context. Readers will discover sentiment mining techniques that can be exploited for the creation and automated upkeep of review and opinion aggregation websites, in which opinionated text and videos are continuously gathered from the Web and not restricted to just product reviews, but also to wider topics such as political issues and brand perception.
The book also enables researchers to see how affective computing and sentiment analysis have a great potential as a sub-component technology for other systems. They can enhance the capabilities of customer relationship management and recommendation systems allowing, for example, to find out which features customers are particularly happy about or to exclude from the recommendations items that have received very negative feedbacks. Similarly, they can be exploited for affective tutoring and affective entertainment or for troll filtering and spam detection in online social communication."Descrizione articolo" può appartenere a un’altra edizione di questo titolo.