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ByteStack’s sentiment analysis uses an AI model to read every mention it collects and classify it as positive, negative, or neutral. When you include a sentiment instruction in your query prompt, the API returns raw mention counts alongside a per-category breakdown and an overall sentiment score — giving you a single, comparable metric you can track over time.

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Request and interpret sentiment analysis

1

Include sentiment in your query prompt

Add explicit classification instructions to your natural language prompt. ByteStack uses this to apply the sentiment model before returning results.
curl -X POST https://api.bytestack.dev/v1/queries \
  -H "Authorization: Bearer $BITKIT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "What'\''s the overall sentiment around our product launch on Reddit and X this week? Classify mentions as positive, negative, or neutral.",
    "sources": ["reddit", "x"]
  }'
2

Understand the sentiment breakdown response

ByteStack returns a sentiment object alongside the standard mention counts. Each category includes a count and a representative sample of mentions.
{
  "query_id": "qry_02k8au4ql3examp2f",
  "status": "completed",
  "result": {
    "total_mentions": 2104,
    "unique_users": 1387,
    "sentiment": {
      "sentiment_score": 0.61,
      "positive_count": 1283,
      "negative_count": 412,
      "neutral_count": 409,
      "examples": {
        "positive": [
          "The new release is genuinely impressive — performance is night and day.",
          "Finally launched and it did not disappoint. Great work by the team."
        ],
        "negative": [
          "Onboarding is still confusing. Took me 20 minutes to find the export option.",
          "Pricing jumped without warning. Not happy about this."
        ],
        "neutral": [
          "ByteStack just dropped v1.4. Anyone tried it yet?",
          "Saw the announcement. Will check it out this weekend."
        ]
      }
    }
  }
}
The sentiment_score ranges from 0.0 (entirely negative) to 1.0 (entirely positive). A score above 0.5 means positive mentions outnumber negative ones. Use positive_count, negative_count, and neutral_count for absolute comparisons across campaigns or time periods.
3

Visualize in the dashboard

After the query completes, open Dashboard → Jobs and select the result. ByteStack renders a Sentiment Breakdown chart showing the share of positive, negative, and neutral mentions. Hover over any segment to see the count and the top example mentions for that category.You can pin the chart to your workspace overview for at-a-glance monitoring during a launch or campaign.
4

Export for deeper analysis

To run your own analysis or load results into a BI tool, download the structured JSON from Storage or use the REST API.
curl -X GET "https://api.bytestack.dev/v1/storage/qry_02k8au4ql3examp2f" \
  -H "Authorization: Bearer $BITKIT_API_KEY" \
  --output sentiment-results.json
The exported file contains the full sentiment object, all mention metadata, and timestamps so you can join results across multiple query runs.
Combine sentiment with competitor queries to benchmark your brand’s perception against rivals. For example: “Compare the sentiment around YourBrand and CompetitorBrand on Reddit this month. Classify mentions as positive, negative, or neutral.” The response includes a side-by-side sentiment_score for each brand, making it easy to spot where a competitor has an edge.