Introduction to Text Analytics with R – Part 3: Data Pipeline

This data science tutorial 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 accuracy of the trained classification models

Part 3 of this video series provides an introduction to the video series and includes specific coverage:

– Exploration of textual data for pre-processing “gotchas”
– Using the quanteda package for text analytics
– Creation of a prototypical text analytics pre-processing pipeline, including (but not limited to): tokenization, lower casing, stop word removal, and stemming.
– Creation of a document-frequency matrix used to train machine learning models

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

The data and R code used in this series is available via the public GitHub:
https://github.com/datasciencedojo/IntroToTextAnalyticsWithR

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- Data Science Dojo is a paradigm shift in data science learning. We enable all professionals (and students) to extract actionable insights from data.

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