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

In the world of quant development, being able to gauge public sentiment is crucial, especially when it comes to environmental topics that can influence market trends. However, scraping news articles or social media for sentiment analysis can be a real pain. It often involves dealing with rate limits, parsing HTML, and cleaning data. Fortunately, there's a more straightforward solution.

The Problem (DIY scraping pain)

When you try to scrape sentiment data, you face several challenges:

1. Complexity: You need to handle various data formats and structures.
2. Rate Limits: Many websites limit how often you can request data.
3. Data Quality: Scraped data often requires extensive cleaning to be useful.
4. Real-time Updates: You need a reliable way to get up-to-date sentiment data without constant manual checks.

These challenges can waste valuable development time and slow down your decision-making process.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. This API offers a clean and simple solution to gather sentiment data. With just one endpoint, you can get sentiment scores, momentum, clusters, and confidence levels—all essential for making informed decisions.

Endpoint

The specific endpoint we'll use is /news_semantic, which gives you a broad view of public sentiment around environmental topics.

The Code (Python GET /news_semantic with code blocks)

Let's dive into how to use this API in Python. First, ensure you have the requests library installed:

pip install requests

Here's a simple script to fetch sentiment data:

import requests

def get_environment_sentiment(api_key):
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": f"Bearer {api_key}"
    }

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

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error: {response.status_code} - {response.text}")

if __name__ == "__main__":
    API_KEY = "YOUR_API_KEY"  # Replace with your actual API key
    sentiment_data = get_environment_sentiment(API_KEY)
    print(sentiment_data)

The above code defines a function to call the Pulsebit API and fetch the sentiment data.

Reading the Response

The response you get contains several important fields:

• sentiment: This field indicates the overall sentiment score. For example, +0.00 means neutral sentiment.
• momentum: This indicates the rate of change in sentiment; a value of +1.40 suggests a positive upward trend.
• clusters: This tells you how many clusters of sentiment data were identified. 0 in this case indicates no distinct clusters.
• confidence: This represents how confident the model is in its sentiment assessment; a score of 0.87 signifies high confidence.

Three Use Cases

1. Algo Alert: Use the momentum and sentiment score to trigger alerts for trading decisions. For example, if momentum surpasses a certain threshold, you could buy or sell assets related to environmental sectors.

2. Slack Bot: Create a Slack bot that posts sentiment updates based on the API data. This allows your team to stay informed about shifts in public sentiment regarding environmental issues in real-time.

3. Dashboard: Build a dashboard using a framework like Dash or Streamlit to visualize sentiment trends over time. Include charts for sentiment scores and momentum to easily track changes.

Get Started

If you're interested in implementing this, check out the Pulsebit API documentation. It provides details on how to set up your API key and additional functionalities you can explore.

In summary, using the Pulsebit API can significantly streamline your workflow for tracking environmental sentiment. No more DIY scraping headaches—just reliable, structured data at your fingertips. Happy coding!