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

In the ever-evolving world of energy markets, staying ahead means keeping an eye on sentiment shifts. Manually scraping news sites for sentiment analysis can be a labor-intensive and error-prone process. Instead, leveraging the Pulsebit API can streamline this task significantly.

The Problem

DIY scraping for sentiment analysis is fraught with challenges. Websites change their structures, which breaks your scrapers; maintaining the code can quickly become a full-time job. Additionally, the quality of sentiment analysis can vary widely depending on the tools and algorithms you use. You might find yourself spending more time fixing issues than gaining actionable insights.

The Solution

Enter the Pulsebit API. This powerful API provides a single endpoint to access sentiment analysis data, making it easy to get real-time insights into energy sentiment. The endpoint we will be focusing on is /news_semantic. It provides a streamlined way to gather sentiment data with minimal hassle.

The Code

Here’s how to use the Pulsebit API with Python to retrieve sentiment data for the energy sector. Make sure you have the requests library installed:

pip install requests

Now, you can use the following code to make a GET request to the /news_semantic endpoint:

import requests

# Replace 'YOUR_API_KEY' with your actual Pulsebit API key
API_KEY = 'YOUR_API_KEY'
BASE_URL = 'https://api.pulsebit.co/news_semantic'

def get_energy_sentiment():
    headers = {
        'Authorization': f'Bearer {API_KEY}',
        'Content-Type': 'application/json',
    }

    response = requests.get(BASE_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()
    print(sentiment_data)

Reading the Response

The response from the Pulsebit API contains several key fields. Here's a breakdown of what to expect:

• energy_sentiment: The overall sentiment score for the energy sector. For example, +0.00 indicates neutral sentiment.
• momentum: This reflects the trend in sentiment. A value of +0.71 suggests positive momentum, indicating that sentiment is moving in a favorable direction.
• clusters: The number of distinct sentiment groups identified. A value of 0 means no significant clusters were detected.
• confidence: This indicates the reliability of the sentiment analysis. A score of 0.87 suggests high confidence in the results.

Three Use Cases

1. Algo Alert: Set up a trading algorithm that triggers alerts when the energy sentiment shifts beyond a certain threshold. For instance, if the momentum exceeds +0.50, your algorithm can initiate a buy order.

2. Slack Bot: Create a Slack bot that posts daily sentiment updates. This keeps your team informed about market trends without needing to manually check sources. Use the sentiment data to generate a concise message like: "Today's energy sentiment is neutral with positive momentum."

3. Dashboard: Integrate the data into a dashboard using a visualization library like Dash or Flask. Display sentiment trends over time, helping stakeholders make informed decisions.

Get Started

Ready to dive in? Head over to Pulsebit Documentation to get your API key and explore more endpoints. This service can help you automate sentiment analysis and save you a considerable amount of time.

By leveraging the Pulsebit API, you can eliminate the headaches of manual sentiment analysis, allowing you to focus on what matters most: making data-driven decisions in the energy market.