This learning path provides a comprehensive introduction to text mining concepts and their practical application using R. It covers essential techniques such as text preprocessing (lower case conversion, punctuation and stopword removal, tokenization), stemming, lemmatization, feature extraction (Bag of Words, TF-IDF), and advanced models like Word2Vec, Doc2Vec, Sentiment Analysis, Latent Semantic Analysis, and Latent Dirichlet Allocation, all with a focus on implementation in R.

• Understand fundamental text mining concepts.

• Apply text preprocessing techniques in R (lower case conversion, punctuation and stopword removal, tokenization).

• Implement stemming and lemmatization in R.

• Utilize feature extraction methods like Bag of Words and TF-IDF in R.

• Work with advanced text mining models such as Word2Vec and Doc2Vec in R.

• Perform sentiment analysis using R.

• Apply Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) in R for topic modeling.