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

The Problem

If you’ve ever tried scraping sentiment data from various news sources, you know it’s a tedious and error-prone process. You might spend hours building scrapers, only to find that the data you’ve gathered is inconsistent or outdated. The complexity ramps up when you want to analyze trends over time, as you’ll need to clean and normalize your data. This is where the frustration often sets in. Wouldn't it be nice to have a single source for reliable sentiment data?

The Solution

Enter the Pulsebit API. With just one endpoint, you can retrieve sentiment data that’s both timely and structured. This week, the Pulsebit API reported a sentiment score of +0.00 for the topic of science, with a momentum of +1.50 over 24 hours. This is particularly noteworthy because the sentiment score is neutral, yet the momentum indicates a rising interest in science topics.

API Endpoint

You’ll be using the /news_semantic endpoint to gain insights into sentiment shifts. Here’s how to do that in Python.

The Code

First, ensure you have the requests library installed. If you don't have it yet, run:

pip install requests

Now, here’s a simple script to fetch sentiment data using the Pulsebit API:

import requests

# Define the Pulsebit API endpoint
url = 'https://pulsebit.lojenterprise.com/api/v1/news_semantic'

# Make the GET request
response = requests.get(url, params={'topic': 'science'})

# Check for successful response
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print(f"Error: {response.status_code}")

Reading the Response

Let’s break down the response you’re getting from the API:

• sentiment_score: This is currently +0.000. It indicates neutral sentiment, but don’t let that fool you; the momentum tells a different story.
• momentum_24h: This is +1.500, suggesting a significant upward trend in discussions surrounding science.
• confidence: At 0.870, this high value indicates that the data is reliable. You can trust that this sentiment score reflects the current sentiment accurately.
• sentiment_index_0_100: A score of 70.0 shows that, despite being neutral, the general trend is leaning positively.
• direction: This is marked as rising, which is critical. It means the interest in science is not just stable; it’s growing.
• semantic_clusters: With 20 clusters, you can delve into various topics within the science domain, enriching your analysis.
• region: This data is specific to the US, which is useful if you’re targeting a specific audience.
• semantic_similarity_avg: At 0.230, this suggests that there’s a moderate level of similarity among the clustered topics.

Three Use Cases

Now, how can you put this data to work? Here are three practical applications:

1. Algorithmic Alerts: Set up an alert in your trading or analysis algorithms to notify you when momentum exceeds a certain threshold, say +1.00. This could help you act quickly on shifts in public sentiment.

2. Slack Bot: Build a Slack bot that posts daily updates on science sentiment. If the sentiment score suddenly spikes or drops, it can notify your team so you can discuss it in real time.

3. Dashboard Visualization: Create a dashboard that visualizes sentiment trends over time. Use the momentum and sentiment score data to provide insights at a glance, helping you make informed decisions.

Get Started

You can begin using the Pulsebit API by checking out their documentation here. The one-stop endpoint simplifies your workflow, letting you focus on what matters: analyzing sentiment data effectively without the hassle of scraping.

By leveraging the Pulsebit API, you can transform your approach to sentiment analysis with minimal effort and maximum insight. Now, go ahead and start building!