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

In the world of quantitative finance and data analysis, staying ahead of market sentiment is crucial. One of the areas gaining traction is environmental sentiment analysis, which can help you gauge public perception about environmental issues and investments. In this post, I'll walk you through how to use the Pulsebit API to detect shifts in environmental sentiment using Python.

The Problem (DIY scraping pain)

Traditionally, gathering sentiment data required a DIY approach involving web scraping from various news sources and social media platforms. Not only is this prone to errors, but it also consumes valuable time and resources. Furthermore, maintaining a scraper can be tedious, as websites frequently change their structures, breaking your code.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. It simplifies the sentiment analysis process with a single endpoint that provides you with the necessary data to understand environmental sentiment shifts. The GET /news_semantic endpoint returns a structured response, including sentiment scores, momentum, and confidence levels, all in one place.

The Code (Python GET /news_semantic)

Here’s a quick example of how to fetch environmental sentiment data using Python and the requests library:

import requests

def get_environment_sentiment():
    url = "https://api.pulsebit.co/v1/news_semantic"
    params = {
        "topic": "environment"
    }
    headers = {
        "Authorization": "Bearer YOUR_API_KEY"
    }

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

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

data = get_environment_sentiment()
print(data)

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

Reading the Response

When you make a successful GET request, you will receive a JSON response that looks something like this:

{
    "sentiment": 0.00,
    "momentum": 1.40,
    "clusters": 0,
    "confidence": 0.87
}

Here's a breakdown of each field:

• sentiment: This is the overall sentiment score for the environment topic. A score of 0.00 indicates neutral sentiment.
• momentum: This indicates the change in sentiment momentum. A positive value, like 1.40, suggests increasing positive sentiment.
• clusters: This shows how many sentiment clusters have been identified. A value of 0 means no distinct clusters were found.
• confidence: This percentage (e.g., 0.87 or 87%) indicates the reliability of the sentiment score. A higher value means greater confidence in the accuracy of the analysis.

Three Use Cases

1. Algo Alert: Set up an algorithmic trading alert based on sentiment shifts. For instance, if the sentiment score drops below a certain threshold, trigger a sell signal. You can regularly poll the API and implement logic to react accordingly.

2. Slack Bot: Create a simple Slack bot that notifies your team of significant shifts in environmental sentiment. If the momentum exceeds a certain value (like 1.40), send a message to a specific channel.

3. Dashboard: Integrate the Pulsebit API into a dashboard application where you visualize sentiment trends over time. You can use libraries like Dash or Streamlit to create interactive graphs that showcase changes in sentiment and momentum.

Get Started

To dive deeper into the Pulsebit API, visit the official documentation. You’ll find detailed information on how to authenticate, use various endpoints, and optimize your requests.

In summary, the Pulsebit API provides a streamlined approach to capturing environmental sentiment data. By utilizing the GET /news_semantic endpoint, you can eliminate the pain of DIY scraping and focus on making actionable insights in your projects.