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

The Problem

If you've ever tried scraping sentiment data for climate topics, you know the struggle: constant changes in website structures, rate limits, and the endless need to manage proxies. It's a tedious DIY task that can consume too much of your valuable time. Plus, the accuracy of your data is often questionable. What if you could bypass all this hassle and get reliable sentiment data with just a simple API call?

The Solution

Enter the Pulsebit API. With one powerful endpoint, you can retrieve sentiment data for various topics, including climate. Why spend hours scraping when you can get structured, reliable data in seconds? Today, let’s explore how to use the Pulsebit API to detect shifts in climate sentiment, especially in light of the current data: a sentiment score of +0.26 with a momentum of +1.22 suggests a noteworthy upward trend in climate sentiment.

The Code

Here's how to make a GET request to the /news_semantic endpoint of the Pulsebit API using Python:

import requests

def get_climate_sentiment():
    url = "https://pulsebit.lojenterprise.com/api/news_semantic"
    params = {
        'topic': 'climate'
    }
    headers = {
        'Authorization': 'Bearer YOUR_API_KEY'  # Replace with your API key
    }

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

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

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

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

Reading the Response

When you make the API call, you'll receive a JSON response with several fields. Here's how to interpret each one from the current data:

• sentiment_score: +0.258 - This score indicates a positive sentiment towards climate topics. The baseline sentiment score typically hovers around 0, so this is a solid upward shift.
• momentum_24h: +1.217 - This metric shows a significant increase in sentiment momentum over the last 24 hours. Compare this to previous periods, and you’ll see a notable spike, suggesting an urgent need to pay attention.
• confidence: 0.870 - A high confidence level indicates that the data is reliable. You can trust that the sentiment analysis is well-grounded.
• sentiment_index_0_100: 62.92 - This index quantifies sentiment on a scale of 0 to 100. A score above 50 indicates a predominantly positive sentiment, further validating the upward trend.
• direction: rising - This is straightforward but vital. A rising sentiment direction tells you to keep your eyes peeled for further developments.
• semantic_clusters: 0 - This means there are no distinct clusters in the prevailing sentiment, indicating a broad consensus rather than fragmented opinions.
• region: global - This shows that the sentiment trend is not limited to a specific geographical area.
• semantic_similarity_avg: 0.224 - While this is lower than expected, it indicates some diversity in the language used around climate discussions.

Three Use Cases

1. Algo Alert: Set up an alert in your trading or news aggregation algorithm. When sentiment momentum exceeds a certain threshold (like +1.22), trigger an action or notification to dive deeper into the news.

2. Slack Bot: Create a Slack bot that posts daily updates on climate sentiment. Using the Pulsebit API, you can easily inform your team about shifts in public sentiment, enhancing collaborative decision-making.

3. Dashboard: Integrate the sentiment data into a dashboard using tools like Dash or Streamlit. Visualize the sentiment score and momentum trends over time, allowing for quick assessments of public opinion.

Get Started

Ready to harness the power of sentiment analysis? Check out the Pulsebit API documentation for more endpoints and detailed usage instructions. It’s time to elevate your climate sentiment monitoring without the scraping headaches.