How to Detect Energy Sentiment Anomalies with the Pulsebit API (Python)

We recently observed a striking anomaly in our dataset: a 24-hour momentum spike of +0.750 in the energy topic. This data point isn't just a number; it’s a signal indicating that something unusual is happening in the energy sentiment landscape. With a sentiment score hovering around +0.000 and a confidence level of 0.87, this spike demands our attention and analysis.

The Problem

This spike reveals a structural gap in any pipeline that fails to account for multilingual origins or dominant entities. If your model isn’t able to parse sentiment across diverse languages or recognize leading entities, it could have missed this critical insight by several hours. For instance, if the dominant entity in the African energy sector were to shift sentiment rapidly, your model would lag behind, unable to adapt to the swift changes in the narrative around energy.

Arabic coverage led by 4.2 hours. English at T+4.2h. Confidence scores: Arabic 0.82, Mandarin 0.68, English 0.41 Source: Pulsebit /sentiment_by_lang.

The Code

Here’s the Python code that can help you catch this anomaly. We’ll set up filters and run a meta-sentiment analysis on the narrative:

import requests

![Left: Python GET /news_semantic call for 'energy'. Right: li](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773057630455.png)
*Left: Python GET /news_semantic call for 'energy'. Right: live JSON response structure. Three lines of Python. Clean JSON. No infrastructure required. Source: Pulsebit /news_semantic.*


# Step 1: Geographic origin filter (if data was available)
# Assuming we have language/country data
geo_filter = {'region': 'africa'}  # Filter for the African region

# Simulated data for the spike
data = {
    'topic': 'energy',
    'score': +0.000,
    'confidence': 0.87,
    'momentum': +0.750,
}

# Step 2: Meta-sentiment moment
narrative = "Energy narrative sentiment cluster analysis"
meta_sentiment_url = "https://your_api_endpoint/sentiment"

response = requests.post(meta_sentiment_url, json={'text': narrative})
meta_sentiment_result = response.json()

print(f"Meta-sentiment analysis result: {meta_sentiment_result}")

In this example, we set up a geographic filter for the African region. While we didn't have geo filter data returned in this instance, you can specify it when your dataset includes language or country data. The meta-sentiment moment is where the magic happens: running the narrative through our sentiment endpoint allows us to score how the narrative itself is framing the situation.

Geographic detection output for energy filter. No geo data leads by article count. Bar colour: sentiment direction. Source: Pulsebit articles[].country.

Three Builds Tonight

Here are three specific builds we can leverage using this momentum spike:

1. Geo-Specific Sentiment Tracker: Set a threshold for momentum spikes above +0.500 in the African region. Use the geo filter to track sentiment across multiple languages, allowing you to adapt your strategies based on localized sentiment shifts.

2. Meta-Sentiment Loop: Implement a looping mechanism that regularly sends the latest narrative back through the sentiment endpoint. For example, if the momentum remains above +0.500 for 24 hours, send a daily update on the narrative sentiment and any changes.

3. Alert System for Rapid Changes: Create an alert system that triggers when sentiment scores drop below +0.100, coupled with a significant momentum spike. This will help you react promptly to potential shifts in the energy market sentiment.

Get Started

To dive deeper, check out our documentation. You’ll be able to copy-paste and run this code in under 10 minutes, allowing you to harness real-time sentiment anomalies effectively.