App reviews thematic dashboard
Reviews analyzed
Average rating
4–5★ share
Multi-theme reviews
Corpus readout
What the reviews are really talking about
Theme coding is multi-label: a single review can contribute to more than one theme, so theme shares do not sum to 100%.
Corpus composition
Apps contributing the most review volume
| App | Reviews | Share | Avg. star |
|---|
1. Rating distribution across the full corpus
Before reading themes, this shows the baseline tone of the dataset.
2. Review volume over time with average rating
Volume peaks and rating shifts are easier to interpret together than separately.
3. Recurring themes in the review text
This surfaces the most repeated concern and praise patterns from the corpus.
4. How each theme maps to star ratings
Some themes are praise-heavy, while others are strongly associated with low-star reviews.
5. Theme trajectory over time
This traces the main complaint/request themes quarter by quarter.
6. Words that separate unhappy and happy reviews
Lexical contrast provides direct evidence for the themes instead of abstract labels alone.
Representative evidence
Illustrative excerpts for each theme
| Theme | Excerpt | Star | App | Date |
|---|
Interpretation
How to read the theme mix
Method
How the thematic coding was built
This dashboard uses a rule-based, multi-label thematic pass over the review text, grounded in repeated phrases and domain terms found in the corpus. Reviews can activate multiple themes when they discuss both product experience and functional needs.
The chart sequence is deliberate: 2 overview charts, 3 thematic charts, and 1 evidence chart, so each view answers a different analytical question rather than repeating the same story.