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

In the world of finance and app development, monitoring sentiment shifts in mobile platforms can provide a significant edge. However, DIY scraping for sentiment analysis can be cumbersome and unreliable. In this article, we’ll explore how to leverage the Pulsebit API to get real-time sentiment data effortlessly.

The Problem (DIY scraping pain)

Scraping sentiment data from social media, forums, and news sites can be a hassle. You might have to deal with:

• Rate limiting from websites.
• Constant changes in page structure.
• Errors in data parsing.
• Legal and ethical concerns.

All of this can consume valuable development time and lead to inconsistent results. Instead of spending hours scraping and cleaning data, why not use a dedicated API? This is where the Pulsebit API comes in handy.

The Solution (Pulsebit API — one endpoint)

The Pulsebit API provides a single endpoint that allows you to fetch sentiment data directly. For mobile sentiment, you can use the /news_semantic endpoint, which gives you structured information about sentiment shifts, momentum, and more.

Example API Call

Here’s how you can make a simple GET request to the Pulsebit API:

import requests

def get_mobile_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}")

# Usage
api_key = "YOUR_API_KEY"
sentiment_data = get_mobile_sentiment(api_key)
print(sentiment_data)

The Code (Python GET /news_semantic)

In the code snippet above, we define a function get_mobile_sentiment that takes an API key as an argument. It makes a GET request to the /news_semantic endpoint and returns the JSON response.

Dependencies

Make sure you have the requests library installed:

pip install requests

Reading the Response

Let’s break down the response you’ll get from the Pulsebit API based on the current data:

{
  "mobile_sentiment": 0.00,
  "momentum": 1.30,
  "clusters": 0,
  "confidence": 0.87
}

• mobile_sentiment: This field indicates the overall sentiment for mobile, which is currently 0.00. This value can range from -1 (very negative) to +1 (very positive).
• momentum: This represents the rate of change in sentiment. A value of 1.30 suggests a positive shift in sentiment momentum.
• clusters: This indicates the number of clusters of sentiment data detected. Here, it’s 0, meaning there are no significant clusters to analyze.
• confidence: This is the confidence level of the sentiment analysis, which is currently 0.87. A higher value indicates greater reliability in the sentiment data.

Three Use Cases

1. Algo Alert: Set up an algorithmic trading alert when the mobile sentiment crosses a certain threshold. For instance, alert when sentiment goes above 0.50 or drops below -0.50.

2. Slack Bot: Create a Slack bot that sends a daily summary of mobile sentiment. This can help your team stay updated on market trends without manual checks.

3. Dashboard: Integrate the sentiment data into a dashboard (e.g., using Dash or Flask) for visual monitoring. This allows you to track sentiment shifts in real-time.

Get Started

To start using the Pulsebit API, visit their documentation. You’ll find detailed information about all available endpoints, including usage limits and authentication methods.

By leveraging the Pulsebit API, you can simplify sentiment analysis for mobile platforms and focus on building your application or trading strategy rather than wrestling with data scraping. Happy coding!