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

The Problem (DIY Scraping Pain)

As developers, we often need to analyze sentiment around specific topics, and immigration is a hot-button issue. Traditionally, this meant scraping news articles, social media, and other sources to gather sentiment data. This DIY approach can be time-consuming, error-prone, and requires constant maintenance to adapt to changes in website structures or API policies.

Instead of spending hours writing and debugging scrapers, we can leverage the Pulsebit API, which provides a straightforward way to access sentiment analysis for various topics, including immigration. The current data shows immigration sentiment at +0.00, with a momentum of +0.20, 0 clusters, and a confidence level of 0.87. Let’s see how we can tap into this data programmatically.

The Solution (Pulsebit API — One Endpoint)

The Pulsebit API offers a single endpoint that returns sentiment data based on real-time news analysis. This makes it a powerful tool for quickly gauging public sentiment without the hassle of data collection and preprocessing.

Endpoint

• GET /news_semantic: This endpoint returns the semantic analysis of news articles.

The Code (Python GET /news_semantic)

To get started with the Pulsebit API, you’ll need to make a simple GET request. Here’s an example of how you can do this in Python:

import requests

# Replace 'your_api_key_here' with your Pulsebit API key
API_KEY = 'your_api_key_here'
BASE_URL = 'https://api.pulsebit.co/news_semantic'

def get_immigration_sentiment():
    headers = {
        'Authorization': f'Bearer {API_KEY}'
    }
    response = requests.get(BASE_URL, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error fetching data: {response.status_code}")

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

Reading the Response

The response from the API will be a JSON object containing several key fields. Here’s what to look for:

• sentiment: The overall sentiment score (e.g., +0.00). This indicates whether the sentiment is positive, negative, or neutral.
• momentum: A value that indicates the speed of sentiment change (e.g., +0.20). A positive momentum suggests increasing positive sentiment.
• clusters: The number of clusters of articles analyzed. In our case, it’s 0, indicating that there might not be enough data to form distinct sentiment groups.
• confidence: A confidence score (e.g., 0.87) that tells you how reliable the sentiment score is. The closer to 1.0, the more reliable the data.

Three Use Cases

1. Algo Alert: You can build a simple alert system that triggers when the sentiment changes significantly. For example, if the sentiment drops below -0.50, you might want to execute a trading strategy based on the sentiment analysis.

2. Slack Bot: Integrate the API with a Slack bot that updates your team on immigration sentiment shifts in real-time. This can help your team stay informed and ready to react.
   

   import slack_sdk

   def send_slack_message(message):
       client = slack_sdk.WebClient(token='your_slack_token')
       client.chat_postMessage(channel='#your-channel', text=message)

   if sentiment_data['sentiment'] < -0.50:
       send_slack_message("Alert: Immigration sentiment dropped significantly!")

1. Dashboard: Create a dashboard to visualize sentiment trends over time. Use libraries like Dash or Streamlit to plot sentiment scores and momentum, allowing your team to track shifts easily.

Get Started

For more information on how to use the Pulsebit API, check out their documentation at pulsebit.co/docs. With just a few lines of code, you can harness the power of real-time sentiment analysis without the hassle of DIY scraping.

By using the Pulsebit API, you can save time and focus on what matters—making informed decisions based on accurate sentiment data.