N-grams – Introduction to Text Analytics with R
N-grams includes specific coverage of:
• Validate the effectiveness of TF-IDF in improving model accuracy.
• Introduce the concept of N-grams as an extension to the bag-of-words model to allow for word ordering.
• Discuss the trade-offs involved of N-grams and how Text Analytics suffers from the “Curse of Dimensionality”.
• Illustrate how quickly Text Analytics can strain the limits of your computer hardware.
Kaggle Dataset can be found here
The data and R code used in this series is available here
About the Series
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