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

The Problem

As a developer, you've probably faced the challenge of scraping environmental sentiment data from multiple sources. It’s a tedious process that often leaves you dealing with inconsistent formats, rate limits, and, let’s not even get started on the parsing headaches. You may find yourself spending more time on maintenance than on building your actual application. Wouldn’t it be great to get reliable sentiment data from a single source?

The Solution

Enter the Pulsebit API. With just one endpoint, /news_semantic, you can get comprehensive sentiment analysis on the environment, without the hassle of DIY scraping. Right now, the environment sentiment score is sitting at +0.00 with a solid momentum of +1.40. The confidence level is at 0.87, which is quite strong, and the sentiment index is pegged at 68.75. This indicates a rising sentiment direction across 20 clusters globally. What’s particularly interesting is how the momentum has shifted recently—this indicates a growing interest or concern in environmental topics that you can tap into.

The Code

Let’s dive into how you can utilize the Pulsebit API with Python. You’ll need the requests library, so make sure you have that installed:

pip install requests

Now, here’s a simple script to get the sentiment data:

import requests

def get_environment_sentiment():
    url = "https://pulsebit.lojenterprise.com/api/news_semantic"
    params = {
        'topic': 'environment'
    }

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

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

Reading the Response

When you call the API, you’ll receive a JSON response containing several fields. Here’s what to look for:

• TOPIC: The topic you requested (in this case, it’s always "environment").
• momentum_24h: The change in sentiment momentum over the last 24 hours. A positive value like +1.400 indicates increasing interest.
• sentiment_score: The overall sentiment score, which is +0.000 right now. This is neutral but should be monitored closely.
• confidence: At 0.870, this number indicates how confident the algorithm is in its sentiment analysis.
• sentiment_index_0_100: A normalized score, currently 68.75, suggesting a relatively positive sentiment.
• direction: Currently marked as "rising," indicating an upward trend.
• semantic_clusters: The number of clusters found—20 in this case, which shows diversity in sentiment sources.
• region: This indicates the geographical scope of the data, which is "global" here.
• semantic_similarity_avg: At 0.247, this gives you an idea of how closely related the sentiments are within the clusters.

Three Use Cases

1. Algo Alert: Set up an algorithm that triggers alerts when the momentum exceeds a certain threshold, say +2.0, allowing you to react quickly to significant changes in sentiment.

2. Slack Bot: Build a Slack bot that posts updates about the environmental sentiment every hour, keeping your team informed and engaged with real-time data.

3. Dashboard: Create a dashboard that visually represents the sentiment over time, allowing stakeholders to see trends and shifts in public sentiment related to the environment.

Get Started

Ready to dive in? You can find the complete documentation for the Pulsebit API here. Don’t let the complexity of scraping hold you back—leverage the power of the Pulsebit API to streamline your development process and stay ahead of the curve on environmental sentiment shifts.