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

In the world of finance and data analysis, understanding public sentiment can be a game changer. As a quant developer, I’ve often found myself grappling with the pain of DIY web scraping to gather sentiment data. Thankfully, there's a more efficient way to do it using the Pulsebit API.

The Problem (DIY scraping pain)

Scraping sentiment data from various online sources is not only tedious but also fraught with challenges. Websites change their structures, leading to broken scrapers. Rate limits can throttle your requests, and dealing with CAPTCHAs is a nightmare. All of these factors can lead to unreliable or outdated data. For instance, if you're tracking mobile sentiment, you might need to scrape multiple sources to get an accurate picture, which can lead to inconsistencies.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. It provides a simple way to access sentiment data through a single endpoint. With just a few lines of code, you can retrieve sentiment scores, confidence levels, and more, without the hassle of scraping. The endpoint we'll focus on is /news_semantic, which gives you the latest sentiment analysis.

The Code (Python GET /news_semantic with code blocks)

To get started, you will need to install the requests library if you haven’t already:

pip install requests

Here’s a sample code snippet that demonstrates how to fetch sentiment data using the Pulsebit API:

import requests

def get_mobile_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    params = {
        "topic": "mobile",  # You can adjust this based on your needs
    }
    response = requests.get(url, params=params)

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

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

Reading the Response

When you hit the /news_semantic endpoint, you'll receive a JSON response that typically contains the following fields:

• sentiment: This indicates the overall sentiment score for the specified topic. For example, a mobile sentiment of +0.00 suggests a neutral stance.
• momentum: This shows how fast sentiment is changing. A momentum of +1.30 indicates a positive shift in sentiment.
• clusters: This number indicates how many distinct sentiment clusters were identified. In our case, 0 clusters suggests that there wasn't enough data to form clusters.
• confidence: This is a measure of how certain the system is about the sentiment score, with 0.87 representing a high level of confidence.

Three Use Cases

1. Algo Alert: Create an algorithmic trading alert that triggers when sentiment shifts beyond a certain threshold. If the momentum goes above a specific value, you could automate buy or sell orders.

2. Slack Bot: Integrate the sentiment data into a Slack bot to keep your team updated on market sentiment in real-time. You can set it to send messages when the sentiment changes significantly.

3. Dashboard: Build a dashboard that visualizes sentiment trends over time. This can help you spot patterns and make informed trading decisions.

Get Started

If you’re interested in implementing this in your own projects, the Pulsebit API documentation is a great resource. You can find it at pulsebit.co/docs. This documentation will provide you with details on authentication, additional endpoints, and more usage examples.

In conclusion, leveraging the Pulsebit API can significantly reduce the overhead of sentiment analysis. By using just a simple GET request, you can access real-time sentiment data, allowing you to focus on building smarter trading algorithms instead of wrestling with web scraping. Happy coding!