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

In the ever-evolving landscape of healthcare, understanding sentiment shifts can provide critical insights for decision-makers. However, gathering and analyzing sentiment data can be a cumbersome process if you attempt to do it manually. In this article, I'll introduce you to the Pulsebit API, which simplifies sentiment analysis with a single endpoint. Let’s dive right in.

The Problem (DIY scraping pain)

Manual scraping of healthcare news and sentiment analysis is not only time-consuming but also fraught with challenges. Websites change their structure, and scraping can quickly become obsolete, not to mention the ethical and legal considerations involved. You could spend hours cleaning data, only to find that your analysis doesn’t yield actionable insights.

The Solution (Pulsebit API — one endpoint)

The Pulsebit API offers a streamlined solution with its /news_semantic endpoint that provides sentiment analysis on healthcare topics in real time. This API aggregates data from various sources, applies sentiment analysis, and returns structured results that are easy to work with.

Current Data Example:

• Healthcare Sentiment: +0.00
• Momentum: +0.50
• Clusters: 0
• Confidence: 0.87

This data suggests a neutral sentiment with positive momentum, indicating potential changes on the horizon.

The Code (Python GET /news_semantic)

To get started with the Pulsebit API, you’ll need to perform a simple GET request in Python. Make sure you have the requests library installed:

pip install requests

Here’s how you can fetch sentiment data using the Pulsebit API:

import requests

def get_healthcare_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 fetching data: {response.status_code} - {response.text}")

api_key = "YOUR_PULSEBIT_API_KEY"
sentiment_data = get_healthcare_sentiment(api_key)
print(sentiment_data)

Replace YOUR_PULSEBIT_API_KEY with your actual API key from Pulsebit.

Reading the Response

Upon making a successful request, the API will return a JSON object containing several fields:

{
  "healthcare_sentiment": 0.00,
  "momentum": 0.50,
  "clusters": 0,
  "confidence": 0.87
}

• healthcare_sentiment: This is the overall sentiment score. A score of 0.00 suggests a neutral sentiment at this moment.
• momentum: This indicates the rate of change in sentiment. A value of +0.50 suggests a positive trend.
• clusters: This represents the number of distinct sentiment clusters detected. A count of 0 means no clear clusters were identified.
• confidence: This field indicates the model’s confidence in its sentiment classification. A confidence score of 0.87 means the model is quite certain about its sentiment assessment.

Three Use Cases

1. Algorithmic Alerts: You can set up alerts based on specific thresholds in sentiment or momentum. For example, if sentiment crosses a certain threshold, trigger a notification to your team.

2. Slack Bot: Create a Slack bot that periodically checks the healthcare sentiment and posts updates in a designated channel. This keeps your team informed without needing to check manually.

3. Dashboard Integration: Visualize sentiment data in real-time on a dashboard using libraries like Dash or Streamlit. This can help stakeholders make data-driven decisions quickly.

Get Started

Ready to leverage the Pulsebit API for your healthcare sentiment analysis? Head over to the Pulsebit documentation to learn more about the endpoint and start building your application.

By streamlining the process of sentiment analysis, the Pulsebit API removes the typical pain points associated with DIY scraping while providing reliable and actionable insights. Give it a try — your data-driven decision-making will thank you!