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Text mining and sentiment analysis are powerful techniques in natural language processing (NLP) that allow extracting meaningful insights from textual data. In R, several packages and tools facilitate text mining and sentiment analysis. Below is a comprehensive guide covering the key aspects of text mining and sentiment analysis in R.
I. Text Mining in R:
1. Text Cleaning and Preprocessing:
a. Tokenization:
- Tokenization involves breaking down text into individual words or phrases (tokens).
- R packages:
tokenizers,tm.
b. Lowercasing:
- Convert all text to lowercase to ensure uniformity.
- R functions:
tolower().
c. Removing Stop Words:
- Eliminate common words (e.g., “the,” “and”) that don’t contribute much to the meaning.
- R packages:
tm,quanteda
d. Stemming and Lemmatization:
- Reduce words to their root form (stemming) or base form (lemmatization).
- R packages:
tm,SnowballC,textstem.
