LSI / Related Keyword Finder
Finds semantically related keywords using word embeddings and co-occurrence. Useful for expanding keyword lists and finding related terms to include.
Install
pip install -r requirements.txtRun
python lsi_keyword_finder.py --keyword "cloud hosting" --corpus-urls https://a.com https://b.compython lsi_keyword_finder.py --keyword "project management" --corpus-file content.txt --top 30Export
Add --output report.xlsx to save results as a spreadsheet.
| Flag | Description |
|---|---|
--keyword required | Seed keyword |
--corpus-urls | URLs to build corpus from. Multiple values allowed |
--corpus-files | Files for corpus. Multiple values allowed |
--top | Top (integer) |
--output | Save as XLSX |
python lsi_keyword_finder.py --helpRun before creating content. Use the output to plan which terms to target, group into clusters, and prioritize by difficulty. Export to XLSX and share with your content team.
Deliver keyword research as a paid service. Run the tool on a client's niche, export results, and present clusters as a content roadmap. Charge $100-300 per research package.
Analyze what keywords competitors rank for that you're missing. Feed the output into your content calendar to close gaps systematically.
Combine with other tools for a complete workflow:
Requires: beautifulsoup4, nltk, numpy, pandas, requests, scikit-learn, sentence-transformers. All included in requirements.txt.
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