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- Author: Jeffrey Strickland
- Publisher: Lulu.com
- ISBN-10: 1716896444
- ISBN-13: 9781716896446
- Format: 15.2 x 22.9 x 2.1 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
Data Science Applications using Python and R is the second book in a series that began in 2018. This volume is dedicated to text analytics and natural language processing. Using real data, the author leads the reader through the analysis of Tweet sentiment analysis, banking product-group complaint analysis, presidential debate analysis, and more. The book covers text mining, natural language processing (NLP), vectorizing text data, discrete classifiers, bag-of-words (BOW) models, sentiment analysis, and Latent Dirichlet Allocation (LDA). The book offers complete Python and R code with detail explanations. It is designed for use with Jupyter Notebook and R Studio. It also includes notes on Python and R markdown and features full color graphics and text on heavy paper. All data sets used in the book are downloadable from GitHub. Some data can also be customized and download ed from the Federal Consumer Complaint Data Catalog. Finally, each chapter contains practice exercises.
EXTRA 10 % discount with code: EXTRA
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- Author: Jeffrey Strickland
- Publisher: Lulu.com
- ISBN-10: 1716896444
- ISBN-13: 9781716896446
- Format: 15.2 x 22.9 x 2.1 cm, hardcover
- Language: English English
Data Science Applications using Python and R is the second book in a series that began in 2018. This volume is dedicated to text analytics and natural language processing. Using real data, the author leads the reader through the analysis of Tweet sentiment analysis, banking product-group complaint analysis, presidential debate analysis, and more. The book covers text mining, natural language processing (NLP), vectorizing text data, discrete classifiers, bag-of-words (BOW) models, sentiment analysis, and Latent Dirichlet Allocation (LDA). The book offers complete Python and R code with detail explanations. It is designed for use with Jupyter Notebook and R Studio. It also includes notes on Python and R markdown and features full color graphics and text on heavy paper. All data sets used in the book are downloadable from GitHub. Some data can also be customized and download ed from the Federal Consumer Complaint Data Catalog. Finally, each chapter contains practice exercises.
Reviews