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

In today's fast-paced market, understanding sentiment shifts can be a game-changer for developers and analysts alike. But scraping sentiment data from social media, forums, or news sites can be a cumbersome task. This article will show you how to use the Pulsebit API to detect mobile sentiment shifts effortlessly.

The Problem (DIY scraping pain)

Building your own sentiment analysis tool usually involves scraping data from various sources, cleaning that data, and then analyzing it. The challenges include:

• Data Volume: You have to manage large datasets, which can be overwhelming.
• Maintenance: Websites frequently change their structure, breaking your scraping scripts.
• Sentiment Analysis: Implementing NLP models requires expertise and time.

In short, it’s tedious and inefficient.

The Solution (Pulsebit API — one endpoint)

Enter Pulsebit, which provides a simple API endpoint for sentiment analysis. With just one call, you can get sentiment data without the hassle of building and maintaining your own scraping and analysis framework. The endpoint we’ll use is /news_semantic.

The Code (Python GET /news_semantic)

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

pip install requests

Here's a Python script to fetch mobile sentiment data using the Pulsebit API:

import requests

def get_mobile_sentiment():
    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:
        raise Exception(f"Error fetching data: {response.status_code} {response.text}")

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

Make sure to replace YOUR_API_KEY with your actual API key from Pulsebit.

Reading the Response

When you call the /news_semantic endpoint, you'll receive a JSON response. Here's how to interpret each field:

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

• mobile_sentiment: This indicates the overall sentiment for mobile-related topics. A value of 0.00 suggests neutrality.
• momentum: This number (1.30) shows the rate of change in sentiment. Positive values indicate increasing sentiment, while negative values indicate a decline.
• clusters: The count (0) represents the number of distinct topics or themes identified in the sentiment data. More clusters usually indicate diverse opinions.
• confidence: A confidence score of 0.87 suggests a high level of reliability in the sentiment data.

Three Use Cases

Now that you have the sentiment data, here are three practical use cases:

1. Algo Alert: Set up a trading algorithm that triggers alerts based on significant shifts in the momentum or mobile_sentiment. For example, if momentum exceeds 2.0, you could have the algorithm execute a buy order.

2. Slack Bot: Create a Slack bot that informs your team of sentiment changes. Use the fetched data to send messages when sentiment hits critical thresholds, keeping your team in the loop.

3. Dashboard: Visualize the sentiment data in a dashboard using libraries like Dash or Streamlit. This could provide real-time insights and help stakeholders make informed decisions.

Get Started

Ready to dive into the world of sentiment analysis with Pulsebit? Check out the detailed documentation here. You'll find more endpoints and data types to enrich your applications.

By leveraging the Pulsebit API, you can save time and resources while gaining valuable insights into mobile sentiment shifts. Happy coding!