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

As developers, we often find ourselves scraping data from various websites to gauge sentiment around important topics. This can be a tedious process, requiring constant maintenance and updates as site structures change. If you're interested in environmental sentiment, you might have faced similar challenges.

In this article, I'll show you how to leverage the Pulsebit API to detect shifts in environmental sentiment with minimal friction.

The Problem (DIY scraping pain)

Scraping data manually can lead to various headaches:

• Website changes: A simple change in HTML structure can break your scraper.
• Data inconsistency: Different sources might report sentiment differently, leading to a fragmented view.
• Maintenance: Ongoing updates consume both time and resources.

Instead of wrestling with these issues, let’s turn to a more efficient solution: the Pulsebit API.

The Solution (Pulsebit API — one endpoint)

The Pulsebit API simplifies sentiment analysis by providing a single endpoint to access a wealth of sentiment data. You can get insights into environmental sentiment with just one request, saving you time and ensuring consistent results.

The endpoint we’ll use is /news_semantic, which returns various sentiment metrics including the overall sentiment score, momentum, and confidence level.

The Code (Python GET /news_semantic)

To interact with the Pulsebit API, you can use Python's requests library. Below is a simple example of how to fetch environmental sentiment data:

import requests

def get_environment_sentiment(api_key):
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": f"Bearer {api_key}"
    }
    params = {
        "topic": "environment"
    }
    response = requests.get(url, headers=headers, params=params)

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

# Example usage
API_KEY = "YOUR_API_KEY_HERE"
sentiment_data = get_environment_sentiment(API_KEY)
print(sentiment_data)

Replace YOUR_API_KEY_HERE with your actual API key from Pulsebit.

Reading the Response

The response from the API includes several important fields:

• sentiment: Overall sentiment score (e.g., +0.00 indicates neutral sentiment).
• momentum: The rate of change in sentiment (e.g., +1.40 indicates increasing positivity).
• clusters: The number of clusters found in the data (e.g., 0 means no distinct sentiment groups identified).
• confidence: The confidence level in the sentiment analysis (e.g., 0.87 indicates a high level of certainty).

Here’s an example of how to interpret the response:

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

In this example, a sentiment score of +0.00 means there's no significant positive or negative sentiment. However, a momentum of +1.40 suggests that sentiment is on the rise.

Three Use Cases

1. Algo Alert: Set up an alert that triggers when sentiment shifts significantly. For instance, if the momentum exceeds a certain threshold, you could initiate trades related to environmentally focused stocks.

2. Slack Bot: Create a Slack bot that posts daily updates on environmental sentiment. Use the API to pull data every morning and send a summary to your team.

3. Dashboard: Build a dashboard that visualizes sentiment changes over time. Use libraries like Plotly or Dash to create an interactive experience that can help stakeholders make informed decisions.

Get Started

To dive deeper into the Pulsebit API and explore additional capabilities, head over to the Pulsebit documentation. You'll find comprehensive details on other endpoints and use cases that can empower your projects.

By utilizing the Pulsebit API, you can save time and effort, turning your focus back to building impactful applications instead of managing scrapers. Happy coding!