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Bulk Keyword Tagger

Use cases

Bulk categorising keywords by topic or intent Applying negative keyword classifications Segmenting keywords by product type Tagging keywords for different campaigns

Applies up to 7 tag categories via substring matching - checks if any tag term exists within each keyword string.

Alphabetically sorts words within keywords for matching, converts to lowercase, removes NaN values, and concatenates matching tags with semicolons.

Designed for Google Colab with automatic download.

Jupyter Notebook

Platform

Jupyter Notebook (requires Python environment)

Input

Keywords CSV

Tags CSV with classification columns

Output

Tagged keywords CSV

View Source

Features

  • Up to 7 tag categories (tag_1 through tag_7)
  • Substring matching (tag appears within keyword)
  • Case-insensitive matching
  • Alphabetical word sorting for better matching
  • Semicolon-concatenated multi-tag output
  • Auto skips malformed CSV lines

How to use

  1. 1 Prepare keywords CSV with Keyword column
  2. 2 Create tags CSV with up to 7 tag columns (tag_1, tag_2, etc.)
  3. 3 Upload both files to Colab notebook
  4. 4 Run cells to apply matching
  5. 5 Download your_keywords_tagged.csv

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