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

We recently spotted an intriguing anomaly in our sentiment data: a 24-hour momentum spike of -0.450 for the topic "software." This kind of shift is a significant indicator that something is brewing under the surface, especially with "Software as a Service (SaaS)" trends. The accompanying cluster story revealed zero articles discussing this, indicating a potential disconnect between what’s happening in the market and what’s being reported.

When your pipeline doesn't account for multilingual origins or dominant entity narratives, you risk missing critical signals like this one by several hours. In this case, the leading narrative revolves around "software" and "SaaS," yet the lack of multilingual data leaves a gap in understanding the broader sentiment landscape. If you're not capturing these dimensions, you're missing out on crucial insights that could inform your decisions.

es coverage led by 9.4 hours. id at T+9.4h. Confidence scores: en 0.87, fr 0.85, es 0.85 Source: Pulsebit /sentiment_by_lang.

Here’s how you can catch this anomaly using Python and our API. The first step involves implementing a geographic origin filter, which can be incredibly valuable when you have access to language or country data. Unfortunately, for this specific case, we found that no geo-filter data was returned for our query on "software."

![DATA UNAVAILABLE: countries — verify /news_recent is return
[DATA UNAVAILABLE: countries — verify /news_recent is returning country/region values for topic: software]

import requests

![Left: Python GET /news_semantic call for 'software'. Right: ](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773308369540.png)
*Left: Python GET /news_semantic call for 'software'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# API endpoint and parameters
api_url = "https://api.pulsebit.com/v1/sentiment"
params = {
    'topic': 'software',
    'score': +0.000,
    'confidence': 0.00,
    'momentum': -0.450
}

# Fetch sentiment data
response = requests.get(api_url, params=params)

# Check response
if response.status_code == 200:
    sentiment_data = response.json()
else:
    print("Failed to retrieve data")

Next, we should run the cluster reason string back through the sentiment scoring endpoint to assess the narrative framing itself. This is where things get interesting. We can input the reason string: "Clustered by shared themes: service, (saas), market, expected, reach."

# Meta-sentiment moment
meta_sentiment_url = "https://api.pulsebit.com/v1/sentiment"
meta_reason = {
    'text': "Clustered by shared themes: service, (saas), market, expected, reach."
}

# Fetch meta sentiment data
meta_response = requests.post(meta_sentiment_url, json=meta_reason)

# Check response
if meta_response.status_code == 200:
    meta_sentiment_data = meta_response.json()
else:
    print("Failed to retrieve meta sentiment data")

With these two pieces in place, we can build some powerful insights around this anomaly. Here are three specific builds to consider:

1. Geo-Filtered Alerts: Create a real-time alert system based on geographic regions where sentiment drops below a certain threshold (e.g., momentum < -0.300) for "software." This will help you catch localized anomalies faster.

2. Meta-Sentiment Scoring: Implement a scoring mechanism that triggers an analysis whenever the sentiment around key phrases (like "SaaS" or "software market") shifts dramatically. Use the outputs from the meta-sentiment loop to adjust your narrative framing dynamically.

3. Clustering Themes: Develop a dashboard that visualizes the clustering of themes related to "software." Use the semantic clusters data to identify which themes are gaining traction and which are fading, allowing for proactive adjustments to your strategy.

For those looking to dive deeper, you can start exploring these features and more by visiting our documentation at pulsebit.lojenterprise.com/docs. With just a few lines of code, you can copy-paste and run this in under 10 minutes, setting you up to catch anomalies like the one we just discussed.