keyword-extractor

Keyword extraction from text uses TF-IDF, RAKE, and frequency analysis to identify the most important terms. This Skill automates the process, supporting batch processing and custom stopword lists to isolate core topics and themes from any text.

Analyzing customer feedback for recurring themes is done by applying RAKE and TF-IDF algorithms to survey text. This approach isolates significant multi-word phrases and pain points, providing richer insights than single-word frequency counts.

Yes, generating word clouds from extracted keywords is supported. The Skill uses scikit-learn and nltk for the text analysis processing, then leverages the wordcloud and matplotlib libraries to visually render the extracted terms.

Yes, keyword extraction supports batch processing and custom stopword lists for tailored analysis. You can process multiple documents sequentially and filter out specific irrelevant words to ensure your extracted terms are highly relevant.

You export text analysis results to various formats using pandas by structuring the extracted keywords into DataFrames. This allows you to save the output to formats like CSV for downstream analysis and reporting.