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. Whether you're tracking public opinion on pharmaceutical developments or monitoring reactions to healthcare policies, having an effective way to gauge sentiment is essential.

The Problem (DIY scraping pain)

If you've ever tried to scrape news articles or social media posts for sentiment analysis, you know how cumbersome it can be. Not only do you have to deal with different data formats and sources, but you also need to implement natural language processing (NLP) techniques to analyze sentiment accurately. This can lead to wasted time and unreliable results.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API, which offers a streamlined solution for detecting sentiment shifts with minimal effort. With just one endpoint, /news_semantic, you can retrieve sentiment data, momentum, and confidence levels without the hassle of DIY scraping or building complex models. This API provides a clear and concise way to access the sentiment data you need.

The Code (Python GET /news_semantic)

To get started with the Pulsebit API, you’ll first need to install the requests library if you haven't already:

pip install requests

Now, let's write a simple Python script to make a GET request to the /news_semantic endpoint:

import requests

def get_healthcare_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        'Authorization': 'Bearer YOUR_API_KEY'
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: {response.status_code}")
        return None

if __name__ == "__main__":
    sentiment_data = get_healthcare_sentiment()
    if sentiment_data:
        print(sentiment_data)

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

Reading the Response

When you call the API, you’ll receive a JSON response that contains several key fields. Here’s a breakdown of what each field means:

{
    "healthcare_sentiment": 0.42,
    "momentum": 0.50,
    "clusters": 0,
    "confidence": 0.87
}

• healthcare_sentiment: This is a floating-point number representing the overall sentiment score. Values can range from -1 (very negative) to 1 (very positive). In our example, a score of 0.42 indicates a moderately positive sentiment.

• momentum: This field indicates the rate of change in sentiment over time. A momentum of 0.50 suggests a stable increase in positive sentiment.

• clusters: This shows the number of distinct sentiment clusters identified in the data. A value of 0 indicates that the API did not find any significant clusters in the recent data.

• confidence: This field measures the reliability of the sentiment score. A confidence level of 0.87 means you can be quite assured that the sentiment score is accurate.

Three Use Cases

1. Algo Alert

Set up an algorithmic trading alert that triggers when the healthcare sentiment exceeds a certain threshold (e.g., 0.5). This can help you capitalize on positive trends quickly.

2. Slack Bot

Integrate the Pulsebit API into a Slack bot that alerts your team whenever there's a significant shift in healthcare sentiment. This can facilitate timely discussions and decision-making.

3. Dashboard

Create a dashboard that visualizes sentiment trends over time. By utilizing libraries like Dash or Streamlit, you can display real-time sentiment analysis alongside other relevant metrics.

Get Started

Ready to dive in? Head over to Pulsebit Docs for more details on getting started with the API and explore additional features.

By leveraging the Pulsebit API, you can avoid the complexities of DIY sentiment analysis and focus on what really matters—making informed decisions based on accurate data.