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

In the world of finance and investment, understanding sentiment around sustainability can be a game-changer. However, gathering this data manually can be a tedious process. Let’s dive into how you can automate this using the Pulsebit API.

The Problem (DIY Scraping Pain)

Traditionally, developers and analysts would resort to web scraping or manually sifting through news articles to gauge sentiment on sustainability topics. This process is not only time-consuming but also prone to errors. Additionally, sentiment analysis can be complex due to the nuances in language. If you’re pulling data from multiple sources, you’re likely to end up with inconsistent results.

The Solution (Pulsebit API — One Endpoint)

Enter the Pulsebit API, which simplifies sentiment analysis by aggregating news and providing sentiment insights with just one endpoint. Specifically, the /news_semantic endpoint enables you to fetch sentiment data about sustainability without the hassle of manual data gathering or scraping.

The Code (Python GET /news_semantic)

Here's how to get started with the Pulsebit API in Python to fetch sustainability sentiment data:

import requests

def get_sustainability_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    params = {
        "topic": "sustainability"
    }
    headers = {
        "Authorization": "Bearer YOUR_API_KEY"
    }

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

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"API request failed with status code: {response.status_code}")

data = get_sustainability_sentiment()
print(data)

Make sure to replace YOUR_API_KEY with your actual API key from Pulsebit. This simple function retrieves sustainability sentiment data and prints it.

Reading the Response

The response from the Pulsebit API contains several important fields:

• sentiment: This shows the current sentiment score for sustainability. In our case, it’s +0.00, indicating a neutral sentiment.
• momentum: This indicates the change in sentiment over time. Here, it’s +0.22, suggesting a slight positive shift.
• clusters: This field shows how many distinct sentiment clusters have been identified. With 0 clusters, it implies that there isn’t enough varied sentiment data to form any significant groups.
• confidence: This indicates the reliability of the sentiment score. A confidence level of 0.87 suggests the sentiment data is quite reliable.

Three Use Cases

1. Algo Alert: You could set up an algorithmic trading alert that triggers when sentiment shifts significantly. For instance, if momentum surpasses a certain threshold (e.g., +0.5), your system could automatically notify you to consider potential investments in sustainable companies.

2. Slack Bot: Automate a Slack bot that posts daily sentiment updates. By integrating the Pulsebit API, you can keep your team informed about the latest sustainability sentiment shifts in real-time.
   

   def send_slack_message(message):
       slack_url = "https://hooks.slack.com/services/YOUR_SLACK_WEBHOOK"
       payload = {"text": message}
       requests.post(slack_url, json=payload)
   
   sentiment_data = get_sustainability_sentiment()
   send_slack_message(f"Sustainability Sentiment: {sentiment_data['sentiment']} | Momentum: {sentiment_data['momentum']}")
   

3. Dashboard: Create a dashboard using tools like Dash or Streamlit to visualize sentiment trends. You can plot the sentiment score and momentum over time, allowing for a quick glance at sustainability sentiment.

Get Started

To dive deeper into the capabilities of the Pulsebit API, check out the official documentation. With just a few lines of code, you can automate your sustainability sentiment analysis, freeing up your time for more strategic thinking.

In conclusion, leveraging the Pulsebit API can save you time and enhance your insights into sustainability sentiment shifts. Now, with the right tools at your disposal, you can focus on what truly matters: making informed decisions based on reliable data.