How to Detect Energy Sentiment Shifts with the Pulsebit API (Python)

The Problem

As a developer in the energy sector or someone interested in market trends, staying updated with sentiment around energy is crucial. However, traditional methods like DIY scraping can be a headache. Each website has its own structure and could change without notice, making it unreliable. Plus, parsing and analyzing text data can become complex and tedious.

What if you could streamline this process and get structured sentiment data with a single API call? That’s where the Pulsebit API comes into play.

The Solution

Pulsebit offers a straightforward API to access sentiment data with just one endpoint: /news_semantic. This allows you to retrieve and analyze energy sentiment without the hassle of scraping.

Key Features of the Pulsebit API

• Sentiment Score: Indicates the overall sentiment in the energy sector.
• Momentum: Measures the strength of the sentiment.
• Clusters: Represents distinct groups of sentiment.
• Confidence Level: How reliable the sentiment score is.

For example, you might encounter the following data:

• Energy Sentiment: +0.00
• Momentum: +0.71
• Clusters: 0
• Confidence: 0.87

The Code

Let's get started with a Python example to call the Pulsebit API.

import requests

def get_energy_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": "Bearer YOUR_API_KEY"  # Replace with your actual API key
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: {response.status_code}, {response.text}")
        return None

if __name__ == "__main__":
    sentiment_data = get_energy_sentiment()
    if sentiment_data:
        print(sentiment_data)

Make sure to replace YOUR_API_KEY with your actual API key from Pulsebit.

Reading the Response

When you call the /news_semantic endpoint, you’ll receive a JSON response that looks like this:

{
    "energy_sentiment": 0.00,
    "momentum": 0.71,
    "clusters": 0,
    "confidence": 0.87
}

Explanation of Each Field

• energy_sentiment: A score that indicates the prevailing sentiment. A score of +0.00 suggests neutral sentiment without a clear positive or negative bias.
• momentum: This value (+0.71) indicates a strong positive trend in the sentiment. A higher value means that sentiment is gaining traction.
• clusters: An indicator of the number of distinct sentiment groups. In this case, 0 means no significant clusters were found.
• confidence: A reliability score of 0.87 shows that we can be quite confident about the provided sentiment.

Three Use Cases

1. Algo Alert: Use the sentiment data to trigger alerts in your trading algorithms. For instance, if momentum exceeds a certain threshold, it could signal a buying opportunity.

2. Slack Bot: Create a Slack bot that posts updates whenever there's a significant change in sentiment. This could help your team stay informed in real-time.

3. Dashboard: Integrate the sentiment data into a dashboard for visual representation. You could use libraries like Dash or Streamlit to create an interactive UI for better insights.

Get Started

For more detailed documentation, check out Pulsebit API Docs. This resource will guide you through authentication, available endpoints, and response structures.

In conclusion, leveraging the Pulsebit API allows you to bypass the complexities of scraping and focus on what truly matters: analyzing sentiment shifts in the energy sector. Start building your application today!