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

In the world of quantitative finance and market sentiment analysis, understanding public sentiment can be a game-changer. However, gathering and processing sentiment data has traditionally been a cumbersome task. Let's dive into how we can streamline sentiment analysis using the Pulsebit API.

The Problem (DIY Scraping Pain)

If you've ever tried building a sentiment analysis tool from scratch, you'll know it can be a real pain. DIY scraping requires managing multiple sources, dealing with different data formats, and constantly updating your code to handle changes to those sources.

You might end up with a patchwork of scripts that breaks every time a website changes its layout. And even if you manage to scrape the data, analyzing and interpreting that data effectively requires a solid understanding of natural language processing, which adds another layer of complexity.

The Solution (Pulsebit API — One Endpoint)

Enter the Pulsebit API. With a single endpoint, you can access rich sentiment data without the hassles of scraping and preprocessing. Pulsebit aggregates sentiment from various sources and provides a clean, easy-to-use API that lets you focus on building your applications rather than wrangling data.

Current Data Snapshot

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

With this information at hand, we can start to detect sentiment shifts in mobile markets effectively.

The Code (Python GET /news_semantic)

Here's how you can get started with the Pulsebit API using Python. You'll need the requests library to make your API calls. If you haven't installed it yet, do so with:

pip install requests

Now, let's write the code to fetch sentiment data.

import requests

def get_sentiment_data():
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        'Authorization': 'Bearer YOUR_API_KEY'  # Replace with your API key
    }

    response = requests.get(url, headers=headers)

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

sentiment_data = get_sentiment_data()
print(sentiment_data)

Reading the Response

When you call the /news_semantic endpoint, you will receive a JSON response with various fields. Here’s what each field means:

• Mobile Sentiment: This indicates the overall sentiment score for mobile-related news. In our case, it’s +0.00, suggesting neutrality.

• Momentum: This value (+1.30) reflects the recent trend in sentiment. Positive momentum suggests increasing bullish sentiment, while negative momentum indicates bearish sentiment.

• Clusters: The number of sentiment clusters identified. A value of 0 means no discernible clusters were found in the data, which might suggest a lack of strong sentiment.

• Confidence: This score (0.87) indicates the reliability of the sentiment data. A higher value (close to 1) means you can trust the data is reflective of true market sentiment.

Three Use Cases

1. Algo Alert: Use the sentiment data in your trading algorithms to trigger buy/sell alerts. For example, if momentum exceeds a certain threshold, your algorithm could initiate a buy order.

2. Slack Bot: Create a Slack bot that posts daily sentiment updates. This could be as simple as sending a message with the current mobile sentiment and momentum, helping your team stay informed.

3. Dashboard: Visualize the sentiment data on a dashboard using libraries like Plotly or Dash. It can provide real-time insights and trends, making it easier to track sentiment shifts over time.

Get Started

Ready to integrate the Pulsebit API into your projects? Head over to the Pulsebit Documentation for more details on the API, including authentication, endpoint usage, and response structures.

By leveraging the Pulsebit API, you can save yourself from the hassle of DIY scraping and focus on what really matters: making informed decisions based on sentiment shifts. Happy coding!