Get aggregated sentiment data for a domain
X-API-Key<token>API Key for programmatic access
domainId*stringstartDate?stringStart date for the report range (ISO 8601). On aggregated endpoints, omit to default to the last 6 months (there is no "all time"); an explicit start is honoured at any length.
endDate?stringEnd date for the report range (ISO 8601). Defaults to now when omitted.
models?array<string>Filter by specific models
topicId?stringFilter by specific topic ID
promptId?stringFilter raw results to a single prompt ID. Used by the sentiment heatmap drawer to lazy-load one cell's LLM response instead of bundling all responses in the aggregate.
includeVariation?booleanInclude variation calculation comparing to previous period
truepage?numberPage number for paginated responses
1 <= value1limit?numberItems per page for paginated responses
1 <= value <= 10050marketLanguageCodePairId?stringFilter by specific market/language pair ID
tagIds?array<string>Filter by tag IDs (visibility_result joined with prompt_tag)
Aggregated sentiment data with scores and trends over time
application/json- response
overallScore*numberOverall sentiment score (0-100 scale)
positivePercentage*numberAverage positive sentiment percentage
neutralPercentage*numberAverage neutral sentiment percentage
negativePercentage*numberAverage negative sentiment percentage
overallVariation?numberOverall sentiment score variation compared to previous period
sentimentVariation*Sentiment score variation percentage compared to previous period
availableModels*array<string>List of available models in the data
modelBreakdown*array<>Per-model sentiment scores. Each scan produces one report per model; a model is scored over the reports it appears in.
chartData*array<>Time series data for charts
sentimentBreakdown*array<>Sentiment breakdown with variations
reportCount*numberNumber of reports included in aggregation
dateRange*Date range of included reports
citations?Aggregated citations from all reports
totalAnswers*numberTotal number of LLM answers across all reports
heatmapData?array<>Heatmap data: questions with results per model
heatmapModels?array<string>List of models available in heatmap data
curl -X GET 'https://api.getmint.ai/api/domains/string/sentiment/aggregated'{ "overallScore": 72, "positivePercentage": 62, "neutralPercentage": 28, "negativePercentage": 10, "overallVariation": 3.5, "sentimentVariation": { "positive": 4.5, "neutral": -1.2, "negative": -3.3 }, "availableModels": [ "chatgpt", "claude" ], "modelBreakdown": [ { "model": "chatgpt", "score": 72, "variation": 0 } ], "chartData": [ { "date": "2026-05-25T10:30:00.000Z", "positive": 62, "neutral": 28, "negative": 10 } ], "sentimentBreakdown": [ { "type": "positive", "percentage": 62, "variation": 4.5 } ], "reportCount": 12, "dateRange": { "start": "2026-04-25T00:00:00.000Z", "end": "2026-05-25T10:30:00.000Z" }, "citations": { "items": [ { "domain": "techcrunch.com", "url": "https://techcrunch.com/2026/01/10/footwear-trends", "title": "Footwear Trends 2026", "prompts": [ "What are the best running shoes?" ], "sentiments": [ "positive", "neutral" ], "scores": [ 0.82, 0.74 ], "count": 12, "models": [ "chatgpt", "claude" ], "text": "Nike continues to lead in athletic footwear innovation." } ], "uniqueDomains": 86, "totalCitations": 512 }, "totalAnswers": 480, "heatmapData": [ { "question": "How do customers feel about Nike?", "results": [ { "model": "chatgpt", "promptId": "69b0495f672f51c1d55da2f7", "sentiment": "positive", "status": "green", "toolUsage": [ { "type": "web_search", "parameters": { "query": "Nike customer sentiment 2026" }, "execution_details": { "status": "success" } } ] } ] } ], "heatmapModels": [ "chatgpt", "claude" ]}