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

The Problem

If you're in the startup ecosystem, you know how volatile sentiment can be. Detecting shifts in sentiment can be the difference between seizing an opportunity and falling behind the curve. Traditionally, this meant DIY scraping from multiple news sites, sentiment analysis tools, and endless hours of parsing data. It’s not just tedious; it’s error-prone and resource-intensive.

Instead of reinventing the wheel, what if you could get a comprehensive sentiment analysis through a single API call? Enter Pulsebit.

The Solution

Pulsebit provides a simple API that allows you to analyze news sentiment related to startups. With just one endpoint, you can retrieve sentiment scores, momentum, and more without having to scrape and analyze raw data yourself.

API Endpoint

To fetch the sentiment data for startups, you’ll be using the /news_semantic endpoint. This endpoint returns valuable metrics about the current sentiment surrounding startups, including sentiment score, momentum, cluster data, and confidence level.

The Code

Let’s get started with a simple Python script that performs a GET request to the Pulsebit API. Make sure you have the requests library installed:

pip install requests

Here’s the code to retrieve the sentiment data:

import requests

def get_startup_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:
        sentiment_data = response.json()
        return sentiment_data
    else:
        print(f"Error: {response.status_code} - {response.text}")
        return None

if __name__ == "__main__":
    API_KEY = 'YOUR_API_KEY'  # Replace with your actual API key
    sentiment = get_startup_sentiment(API_KEY)
    print(sentiment)

Ensure you replace YOUR_API_KEY with your actual Pulsebit API key.

Reading the Response

Once you run the code, you’ll receive a response structured like this:

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

Let’s break down each field:

• sentiment: This value reflects the overall sentiment towards startups. A score of 0.00 indicates a neutral sentiment.

• momentum: This value, currently at 0.50, indicates the degree of change in sentiment over time. A positive value suggests increasing positive sentiment momentum.

• clusters: This shows how many distinct clusters of sentiment were detected in the news articles. Here, 0 means no significant clusters were found.

• confidence: A high confidence score (0.87) indicates that the sentiment analysis is likely accurate and reliable.

Three Use Cases

1. Algo Alert: Implement an alert system in your trading algorithms that triggers when sentiment shifts significantly. For instance, have your algorithm watch for changes in momentum above a certain threshold (e.g., momentum > 0.75).

2. Slack Bot: Create a Slack bot that sends daily updates about startup sentiment. By integrating the API into a simple bot, you can keep your team informed in real time.

3. Dashboard: Build a dashboard that visualizes sentiment over time. You could plot the sentiment and momentum using libraries like Matplotlib or Plotly, allowing stakeholders to see trends at a glance.

Get Started

If you’re interested in further exploring the capabilities of the Pulsebit API, check out their documentation. It provides additional endpoints and details on how to leverage their data for your specific needs.

In conclusion, using the Pulsebit API can save you time and yield actionable insights into the startup ecosystem without the pain of DIY scraping. Implement it today and start making data-informed decisions!