Tipo: materialTypeLabelLibro - General
Ubicación Física: 006.312 / S582 2017

Text mining with R : a tidy approach /

Autor: Silge, Julia.
Otros Autores: Robinson, David ( autor ) .
Pié de imprenta: Boston : O'Reilly Media, 2017.
Descripción: 178 páginas ; ilustraciones, gráficas, fotografías en blanco y negro ; 18 x 23 cm.
ISBN: 9781491981658.
Tema(s):
Contenido: 1. The tidy text format. 2. Sentiment analysis with tidy data. 3. Analyzing word and document frequency: tf-idf. 4. Relationships between words: n-grams and correlations. 5. Converting to and from non-tidy formats. 6. Topic modeling. 7 Case study: comparing Twitter archives. 8. Case study: mining NASA metadata. 9. Case study: analyzing usenet text. References.
Resumen: Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. (Taken from the source).

Lista(s) en las que aparece este ítem: Maestría en Psicología
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Tipo de ítem Ubicación actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro - General Libro - General Sede Cra 13
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Colección General 006.312/S582/2017 (Navegar estantería) Ej. 1 Disponible 64058

1. The tidy text format. 2. Sentiment analysis with tidy data. 3. Analyzing word and document frequency: tf-idf. 4. Relationships between words: n-grams and correlations. 5. Converting to and from non-tidy formats. 6. Topic modeling. 7 Case study: comparing Twitter archives. 8. Case study: mining NASA metadata. 9. Case study: analyzing usenet text. References.

Psicología

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. (Taken from the source).

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