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

The Problem

As a developer in the quant space, tracking sentiment around startups can be a daunting task. You might be tempted to scrape news sites or social media for insights, but DIY scraping comes with its own set of challenges:

• Maintenance: Websites change layouts, breaking your scrapers.
• Volume: Handling large volumes of data can become cumbersome.
• Sentiment Analysis: Implementing and tuning a sentiment analysis model can eat up your time.

For those who want to focus on actionable insights rather than data collection headaches, there's a more efficient way.

The Solution

Enter the Pulsebit API. This API provides a single endpoint to get sentiment data and news about startups, freeing you from the complexities of web scraping. With straightforward GET requests, you can retrieve sentiment scores, confidence levels, and more, all without breaking a sweat.

Current Data Snapshot

For example, as of now, the sentiment for startups is at +0.00, momentum is +0.50, with 0 clusters and a confidence level of 0.87. This snapshot gives a clear view of the current landscape.

The Code

Using Python, you can easily interact with the Pulsebit API. Below is a simple GET request to the /news_semantic endpoint:

import requests

API_URL = "https://api.pulsebit.co/news_semantic"
HEADERS = {
    "Authorization": "Bearer YOUR_API_KEY"
}

def get_startup_sentiment():
    response = requests.get(API_URL, headers=HEADERS)

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

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

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

Reading the Response

Once you make the GET request, you’ll receive a JSON response. Here's a breakdown of the key fields you can expect:

• sentiment: The overall sentiment score (e.g., +0.00).
• momentum: Indicates the rate of change in sentiment (e.g., +0.50).
• clusters: Number of sentiment clusters detected (e.g., 0).
• confidence: A measure of how confident the API is in the sentiment score (e.g., 0.87).

For example, a response might look like this:

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

Three Use Cases

1. Algorithmic Alerts: You can set up alerts that trigger when the sentiment shifts above or below certain thresholds. For instance, if the sentiment drops below 0.00 with high confidence, it could be a signal to take action.

2. Slack Bot: Integrate this functionality into a Slack bot that notifies your team about sentiment shifts in real-time. This could be as simple as a daily digest or instant notifications on significant changes.

3. Dashboard Visualization: Use tools like Dash or Streamlit to build a dashboard that visualizes sentiment trends over time. This makes it easier to spot patterns and correlations with market events.

Get Started

Ready to dive into the Pulsebit API? Check out the official documentation at pulsebit.co/docs. You'll find everything you need to get started with API integration and usage examples.

With the Pulsebit API, you can streamline your sentiment analysis process and focus on what really matters: making data-driven decisions. Happy coding!