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AXION

Sentiment Aggregate

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Sentiment API

The Sentiment API provides comprehensive sentiment analysis across social media, news articles, and analyst reports for public companies. These insights are essential for gauging market sentiment, identifying emerging trends, and understanding public perception of specific stocks.

GET/sentiment/:ticker/all

Retrieves comprehensive sentiment analysis across three key data sources: social media (Google, Twitter, Reddit), financial news, and AI-powered analyst reports. This aggregated view provides a holistic understanding of market sentiment towards a specific company.

Path Parameters

ParameterTypeRequiredDescription
tickerstringRequiredStock ticker symbol (e.g., "AAPL", "TSLA", "MSFT")

Examples:

https://api.axionquant.com/sentiment/AAPL/all
https://api.axionquant.com/sentiment/MSFT/all

Response Fields

socialSentiment

Object containing social media sentiment analysis

label string

Overall sentiment label (POSITIVE/NEGATIVE)

score number

Confidence score (0.0-1.0)

breakdown

Detailed breakdown of positive/negative segments

newsSentiment

Object containing financial news sentiment analysis (same structure as socialSentiment)

analystSentiment

Object containing AI-powered analyst sentiment

sentiment string

Analyst sentiment label (POSITIVE/NEGATIVE)

score number

Confidence score (0.0-1.0)

|

All Sentiment Data

Request

Sample code
1from axion import Axion
2client = Axion(api_key='axn_123')
3
4sentiment = client.sentiment.all('AAPL')
5print(sentiment)

Response

{
  "socialSentiment": {
    "label": "NEGATIVE",
    "score": 0.979367434978485,
    "breakdown": {
      "positive": {
        "count": 0,
        "avgScore": 0
      },
      "negative": {
        "count": 100,
        "avgScore": 0.979367434978485
      }
    }
  },
  "newsSentiment": {
    "label": "NEGATIVE",
    "score": 0.952301025390625,
    "breakdown": {
      "positive": {
        "count": 10,
        "avgScore": 0.875148761272431
      },
      "negative": {
        "count": 16,
        "avgScore": 0.952301025390625
      }
    }
  },
  "analystSentiment": {
    "sentiment": "POSITIVE",
    "score": 0.66
  }
}
GET/sentiment/:ticker/analyst

Provides AI-powered sentiment analysis of analyst reports, research notes, and investment recommendations. Offers insights into professional analyst sentiment and forward-looking perspectives on the company.

Path Parameters

ParameterTypeRequiredDescription
tickerstringRequiredStock ticker symbol (e.g., "AAPL", "TSLA", "MSFT")

Examples:

https://api.axionquant.com/sentiment/AAPL/analyst
https://api.axionquant.com/sentiment/MSFT/analyst

Response Fields

sentiment string

Analyst sentiment label (POSITIVE/NEGATIVE)

score number

Confidence score (0.0-1.0)

|

Analyst Sentiment

Request

Sample code
1from axion import Axion
2client = Axion(api_key='axn_123')
3
4sentiment = client.sentiment.analyst('AAPL')
5print(sentiment)

Response

{
  "sentiment": "NEGATIVE",
  "score": 0.31
}
GET/sentiment/:ticker/news

Analyzes sentiment from financial news articles and press releases. Provides insights into how traditional media is covering the company, including earnings reports, product announcements, and corporate developments.

Path Parameters

ParameterTypeRequiredDescription
tickerstringRequiredStock ticker symbol (e.g., "AAPL", "TSLA", "MSFT")

Examples:

https://api.axionquant.com/sentiment/TSLA/news
https://api.axionquant.com/sentiment/AAPL/news

Response Fields

Same structure as Social Media Sentiment response.

label string

Overall sentiment label (POSITIVE/NEGATIVE)

score number

Confidence score (0.0-1.0)

breakdown

Breakdown of positive/negative segments with counts and average scores

|

News Sentiment

Request

Sample code
1from axion import Axion
2client = Axion(api_key='axn_123')
3
4sentiment = client.sentiment.news('AAPL')
5print(sentiment)

Response

{
  "label": "NEGATIVE",
  "score": 0.982357367873192,
  "breakdown": {
    "positive": {
      "count": 231,
      "avgScore": 0.903340172767639
    },
    "negative": {
      "count": 1292,
      "avgScore": 0.982357367873192
    }
  }
}