How to Create and Apply Transcript Tags
This guide explains how to develop and apply tags to your transcript segments for visualization and analysis.
When You Need This
- You want to visualize patterns across interview segments
- You need to categorize transcript content by topic
- You want to enable filtering and searching by theme
Step 1: Create a Tag Vocabulary File
Before tagging transcript segments, establish a consistent vocabulary:
- Review your transcripts to identify common themes
- Create a list of primary tags (10-20 is usually sufficient)
Step 2: Create filters.csv
to define tags and enable visualization
Create a document that defines your tagging system:
- Create a CSV file named “filters.csv” in your
_data
folder - Structure your file with two columns:
tag
anddescription
- Add your tags, with each row containing:
- A short tag term (used in transcripts)
- A brief description of what the tag represents
Example filters.csv:
tag,description
highlight,Highlight
between,working between media to advance writing process
early,writing before widespread computer usage
paper,using paper in the writing proceess
files,usage and organization of computer files
revision,revision
software,the use of software and/or code for writing
Step 3: Add Tags to Your Transcript CSV
- Open your transcript CSV in a spreadsheet program
- Navigate to the “tags” column
- Add relevant tags to each segment:
- Separate multiple tags with semicolons (e.g., “revision; paper; files”)
- Use the exact tag terms defined in your filters.csv file
- Not every segment needs tags - focus on meaningful content
Tips for Effective Tagging
- Keep it simple - aim for 10-20 primary tags
- Use consistent formatting:
- Lowercase terms
- Avoid special characters
- Use singular forms when possible
- Be selective - tag only the most relevant segments
- Consider audience needs - what will users want to find?
- Be consistent across all transcripts in your collection
How Tags Power Visualization
Once applied, tags enable:
- Color-coded transcript visualizations
- Interactive filtering of content
- Pattern identification across interviews
- Targeted searching within specific themes