27 lines
1.3 KiB
Markdown
27 lines
1.3 KiB
Markdown
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# Podcast Reviews Example
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This is an example data application where we produce text insights from podcast review data. It is made up of N datasets:
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- Raw reviews `(date, podcast, text, rating)`
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- Podcasts `(podcast, title, category)`
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- Categorized review text `(date, category, podcast, text)`
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- Phrase models `(date, category, hash, ngram, score)`
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- Podcast phrase stats `(date, category, podcast, ngram, count, rating)`
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- Podcast daily summary `(date, category, podcast, phrase_stats, recent_reviews)`
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```mermaid
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flowchart LR
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raw_reviews[(Raw Reviews)] & podcasts[(Podcasts)] --> categorize_text --> categorized_texts[(Categorized Texts)]
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categorized_texts --> phrase[Phrase Modeling] --> phrase_models[(Phrase Models)]
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phrase_models & raw_reviews --> phrase_stats --> podcast_phrase_stats[(Podcast Phrase Stats)]
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podcast_phrase_stats & raw_reviews --> calc_summary --> podcast_daily_summary[(Podcast Daily Summary)]
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```
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## Input Data
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Get it from [here](https://www.kaggle.com/datasets/thoughtvector/podcastreviews/versions/28?select=database.sqlite)! (and put it in `examples/podcast_reviews/data/ingest/database.sqlite`)
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## `phrase` Dependency
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This relies on [`soaxelbrooke/phrase`](https://github.com/soaxelbrooke/phrase) for phrase extraction - check out its [releases](https://github.com/soaxelbrooke/phrase/releases) to get a relevant binary.
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