Analyze podcast word usage

To analyze podcast word usage, here’s a step-by-step breakdown of what can be done and how:

1. Transcribe the Podcast

First, obtain a text transcription of the podcast episode. This can be done by:

• Using transcription tools like Otter.ai, Descript, Whisper, or Rev.

• If you already have the transcript, you can upload it here and I’ll proceed with the analysis.

2. Word Usage Analysis

Once you have the transcript, we can analyze it for the following:

a. Word Frequency

• Most commonly used words (excluding stop words like “the”, “and”, etc.)

• Top keywords and recurring themes

b. Unique vs Repetitive Language

• Vocabulary richness (type-token ratio)

• Overused filler words like “um”, “you know”, “like”

c. Sentiment Analysis

• Emotional tone throughout the episode

• Positive vs negative language balance

d. Topic Modeling

• Grouping word clusters to identify main topics

• Tools: Latent Dirichlet Allocation (LDA) or simple co-occurrence analysis

e. Speaker Analysis (if multi-speaker)

• Most used words per speaker

• Speaker-specific patterns or phrases

f. Readability and Style

• Readability score (e.g., Flesch-Kincaid)

• Informal vs formal tone

• Use of storytelling, examples, and rhetorical devices

3. Tools for In-Depth Analysis

• NLTK / spaCy / Gensim (Python) for linguistic and topic modeling

• WordClouds for visualization

• LIWC / IBM Watson Tone Analyzer for sentiment and psychological tone

Next Step:

Upload your podcast transcript or text excerpt here, and I’ll immediately run an analysis based on the above criteria.