Introduction to Text Analytics with R – Part 10: Cosine Similarity

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 10 of this video series includes specific coverage of:

– How cosine similarity is used to measure similarity between documents in vector space.
– The mathematics behind cosine similarity.
– Using cosine similarity in text analytics feature engineering.
– Evaluation of the effectiveness of the cosine similarity feature.

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

Watch the whole series on text analytics

Text Analytics

Next Up is Part 11 of this video series :
https://tutorials.datasciencedojo.com/introduction-to-text-analytics-with-r-part-11-our-first-test/

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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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