Analyze product reviews for sentiment. See trends, identify issues, and understand customer satisfaction at a glance.
Track sentiment changes over time
Top positive and negative phrases from reviews
Breakdown of positive, neutral, and negative reviews
Each review is analyzed for positive, neutral, and negative language patterns using keyword analysis and context detection.
Sentiment score ranges from -1 (very negative) to +1 (very positive). Scores are weighted by review length and rating.
Sentiment is tracked over time to identify whether customer satisfaction is improving or declining.
Top positive and negative phrases are extracted to highlight what customers love and what needs improvement.
The analysis uses keyword-based sentiment detection with confidence scoring. Longer, more detailed reviews tend to be analyzed more accurately. The system learns from star ratings to calibrate scores.
Sarcasm detection is challenging. The system mitigates this by weighting star ratings alongside text analysis. A 1-star review with positive words is recognized as sarcasm.
Yes! You can export sentiment summaries and trends as CSV or JSON for further analysis in Excel, Power BI, or other tools.
The dashboard shows the last 12 time periods (weeks or months). Full historical data is available via API for custom date ranges.
Currently English reviews are fully supported. Multi-language support is planned and can be enabled via API configuration.
Extreme sentiment scores (highly positive/negative with short reviews) can flag suspicious patterns. Combined with other signals, this helps identify potentially fraudulent reviews.