This data science series introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques:
– Tokenization, stemming, and n-grams
– The bag-of-words and vector space models
– Feature engineering for textual data (e.g. cosine similarity between documents)
– Feature extraction using singular value decomposition (SVD)
– Training classification models using textual data
– Evaluating the accuracy of the trained classification models

The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of:
– Overview of the spam dataset used throughout the series
– Loading the data and initial data cleaning
– Some initial data analysis, feature engineering, and data visualization

Kaggle Dataset:
https://www.kaggle.com/uciml/sms-spam...

The data and R code used in this series is available here:
https://code.datasciencedojo.com/data...

Table of Contents:
0:00 Introduction
11:06 Packages
13:21 Read CSV
17:04 Find missing data
19:05 Explore the data
23:14 Text length

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