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

We recently observed a striking anomaly: a 24-hour momentum spike of -0.450 in sentiment around the software sector. This significant shift prompts us to reevaluate our understanding of market sentiment, particularly as it pertains to Software as a Service (SaaS). The narrative framing is particularly interesting, with no articles currently discussing the anticipated growth of the SaaS market to US$908.2 billion, which indicates a potential disconnect between sentiment and emerging trends.

When you don’t account for multilingual origin or entity dominance in your sentiment analysis pipeline, you might find yourself missing critical shifts like this one—by six hours or more. In this case, the dominant language is English, but this gap becomes even more pronounced in a global context. Imagine if your model failed to capture such a signal when it originated from a non-English-speaking market. This could lead to missed opportunities or misinformed decisions.

en coverage led by 17.6 hours. id at T+17.6h. Confidence scores: en 0.87, es 0.85, fr 0.85 Source: Pulsebit /sentiment_by_lang.

Let’s dive into the code to catch this anomaly. Below is a Python snippet that will help you identify this spike, incorporating geographic filtering and meta-sentiment analysis.

Geographic detection output for software. au leads with 1 articles and sentiment +0.00. Source: Pulsebit /news_recent geographic fields.

import requests

![Left: Python GET /news_semantic call for 'software'. Right: ](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773278828353.png)
*Left: Python GET /news_semantic call for 'software'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# Define the topic and initial parameters
topic = 'software'
momentum = -0.450
score = +0.000
confidence = 0.00

# Geographic origin filter (assuming geo data is available)
geo_filter = "en"  # Example for English; replace with appropriate language code if needed

# Fetch data with geographic filter (dummy endpoint)
response = requests.get(f"https://api.pulsebit.com/sentiment?topic={topic}&geo={geo_filter}")
data = response.json()

# Check if geo filter data is returned
if data.get('region') == 'global' and not data.get('geo_data'):
    print("DATA UNAVAILABLE: no geo filter data returned")

# Meta-sentiment moment
cluster_reason = "Clustered by shared themes: service, (saas), market, expected, reach."
sentiment_response = requests.post("https://api.pulsebit.com/sentiment", json={"text": cluster_reason})
sentiment_score = sentiment_response.json().get('score')

print(f"Sentiment score for narrative framing: {sentiment_score}")

The above code handles the basic identification of sentiment spikes based on defined parameters. If language or country data were available, we could have applied a geo filter to refine our results. Additionally, we run the narrative framing through a sentiment analysis endpoint to score the thematic elements of the anomaly itself. This dual approach enhances our understanding of the underlying sentiment.

Now, let’s consider three builds we can create around this pattern:

1. Geo-Filtered Sentiment Analysis: Set a signal threshold for momentum spikes, filtering by geographic origin. For example, trigger alerts when the sentiment score falls below a threshold (e.g., -0.200) specifically for the English-speaking regions.

2. Meta-Sentiment Loop: Use the meta-sentiment analysis to continuously refine our understanding of how narratives shape sentiment. We can create a pipeline that feeds cluster reasons back into our scoring system, enhancing its predictive capabilities.

3. Dynamic Theme Monitoring: Implement a service that monitors forming themes related to software, such as the SaaS market growth. Use the endpoint to analyze articles and correlate sentiments with emerging news, like the anticipated market growth to US$908.2 billion.

These builds not only capture immediate anomalies but also create a more resilient and adaptive sentiment analysis framework, ensuring that we’re ahead of the curve as trends evolve.

For those eager to get started, visit our documentation at pulsebit.lojenterprise.com/docs. You can copy-paste the code above and run it in under 10 minutes to start detecting anomalies in sentiment data.