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

In today’s fast-paced digital landscape, understanding public sentiment around mobile applications can give you a competitive edge. However, scraping sentiment data from various platforms is often cumbersome and error-prone. Let’s explore how to simplify this process using the Pulsebit API.

The Problem (DIY scraping pain)

If you've ever tried to scrape sentiment data manually, you know it can be a real headache. You might find yourself battling with inconsistent data formats, rate limits, and the constant need to update your scraping logic whenever websites change. Not to mention, aggregating sentiment data across different platforms can be a nightmare.

Instead of spending hours on DIY scraping, there’s a better way to harness sentiment data.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. This API provides a streamlined way to access sentiment data with just one endpoint: /news_semantic. You can easily get insights into mobile sentiment, momentum, clusters, and confidence levels— all without having to scrape anything manually.

With the current data showing:

• Mobile sentiment: +0.00
• Momentum: +1.30
• Clusters: 0
• Confidence: 0.87

You can quickly see how the public feels about mobile applications at a glance.

The Code (Python GET /news_semantic)

Now, let’s get into the code. To interact with the Pulsebit API, you can use Python’s requests library. Here's how to make a simple GET request to the /news_semantic endpoint:

import requests

def get_mobile_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception("API request failed with status code: {}".format(response.status_code))

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

This code sends a GET request to the Pulsebit API and returns the response as a JSON object. Make sure you handle any potential errors, as shown.

Reading the Response

When you receive the response, it will look something like this:

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

Here’s what each field means:

• mobile_sentiment: This score indicates the overall sentiment towards mobile applications. A score of 0.00 means neutrality.
• momentum: The momentum score of 1.30 indicates a positive trend in sentiment over time.
• clusters: The number of sentiment clusters detected. A value of 0 suggests that there are no distinct groups of sentiment.
• confidence: A confidence score of 0.87 implies high reliability in the sentiment data provided.

Three Use Cases

1. Algo Alert: You can set up an algorithmic trading alert that triggers based on sentiment changes. For instance, if the mobile_sentiment changes significantly from 0.00, it could indicate a potential buying or selling opportunity.

2. Slack Bot: You can create a Slack bot to notify your team of significant shifts in sentiment automatically. Use a simple polling mechanism to fetch sentiment data at regular intervals and post updates to a dedicated Slack channel.

3. Dashboard: Build a real-time dashboard that visualizes mobile sentiment trends. Using libraries like Dash or Streamlit, you can create a user-friendly interface to display sentiment data graphically.

Get Started

If you're interested in leveraging the Pulsebit API for your projects, check out their official documentation at pulsebit.co/docs. It provides comprehensive guidelines on authentication, available endpoints, and response formats.

By utilizing the Pulsebit API, you can save time and effort, allowing you to focus on what truly matters — building and optimizing your applications based on real-time sentiment data. Happy coding!