Conclusion – Introduction to Text Analytics with R
This video concludes our Introduction to Text Analytics with R and covers:
– Optimizing our model for the best generalizability on new/unseen data.
– Discussion of the sensitivity/specificity tradeoff of our optimized model.
– Potential next steps regarding feature engineering and algorithm selection for additional gains in effectiveness.
– For those that are interested, a collection of resources for further study to broaden and deepen their text analytics skills.
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