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

In today's fast-paced world, staying on top of mobile sentiment can be a game-changer for businesses and developers alike. However, scraping sentiment data manually can be a cumbersome and error-prone task.

The Problem (DIY scraping pain)

Many developers have had the unfortunate experience of creating scrapers to track sentiment on mobile platforms. This often involves dealing with rate limits, inconsistent HTML structures, and the tedious task of parsing and cleaning data. Not to mention the frequent need for updates as website layouts change.

Take a look at the current sentiment data for mobile:

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

You can see how a task like this quickly becomes a headache—not to mention the time wasted in maintaining scrapers.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. Instead of building your own scraping solution, you can rely on a single, well-documented API endpoint that gives you all the sentiment data you need without the hassle. The /news_semantic endpoint is particularly useful for retrieving sentiment data quickly and efficiently.

The Code (Python GET /news_semantic)

Here’s a quick example of how to use the Pulsebit API to get sentiment data. You'll need the requests library for this, so make sure to install it if you haven't already.

import requests

def get_mobile_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    params = {
        'category': 'mobile'
    }
    response = requests.get(url, params=params)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: {response.status_code}")
        return None

# Fetch and display mobile sentiment data
data = get_mobile_sentiment()
print(data)

This code snippet fetches the mobile sentiment data from the API and prints it out. Pretty straightforward, right?

Reading the Response

When you call the /news_semantic endpoint, you’ll receive a JSON response. Let’s break down the key fields you will encounter:

• Mobile Sentiment: This is the overall sentiment score for mobile, which in our example is +0.00. A positive score indicates positive sentiment, while a negative score indicates negative sentiment.

• Momentum: This score (+1.30 in our case) indicates the rate of change in sentiment. A high momentum value suggests that sentiment is shifting rapidly, which could indicate an emerging trend.

• Clusters: This number (0 in our example) represents the number of distinct topics around which the sentiment clusters. No clusters may indicate a lack of significant discussions.

• Confidence: The confidence score (0.87 here) tells you the reliability of the sentiment data. A score closer to 1 indicates high confidence in the sentiment analysis.

Three Use Cases

1. Algo Alert: Set up an alert system that triggers when the momentum exceeds a certain threshold. For example, if momentum is greater than +1.00, you could send an alert to your trading algorithm to make informed decisions.

2. Slack Bot: Build a simple Slack bot that reports daily sentiment shifts. Use the API to fetch data and send a message to a Slack channel summarizing the sentiment changes.

3. Dashboard: Create a real-time dashboard that visualizes mobile sentiment over time. Use libraries like Dash or Streamlit to build a user-friendly interface that displays the sentiment trends along with momentum and confidence levels.

Get Started

For more details on using the Pulsebit API, check the official documentation. It’s straightforward and provides all the information you need to integrate sentiment analysis into your applications.

By leveraging the Pulsebit API, you can save yourself the hassle of DIY scraping and focus on building innovative solutions that take advantage of real-time sentiment data. Happy coding!