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

We just uncovered a significant anomaly: a 24-hour momentum spike of -0.450 in the software sector. This drop signals a noteworthy shift in sentiment surrounding software topics, particularly those clustered around SaaS market growth projections. It’s intriguing that there are currently zero articles discussing this trend, suggesting that the market sentiment is not yet captured in mainstream narratives.

If your sentiment analysis pipeline doesn’t account for multilingual origins or dominant entities, you might have missed this crucial signal by several hours. The primary language here is English, but without a robust mechanism to filter by language or region, you risk overlooking vital insights. This could lead you to misinterpret trends or, worse, make uninformed decisions based on incomplete data.

en coverage led by 19.1 hours. id at T+19.1h. Confidence scores: en 0.86, fr 0.85, es 0.85 Source: Pulsebit /sentiment_by_lang.

To catch anomalies like this, we can leverage our API. Here's a simple Python snippet that identifies the sentiment anomaly. First, we’ll filter by geographic origin, assuming language data is available.

import requests

![Left: Python GET /news_semantic call for 'software'. Right: ](https://aace88ba921016d861eaeb8858550e91.r2.cloudflarestorage.com/pulsebit-marketing/figures/g3_code_output_split_1773273568164.png)
*Left: Python GET /news_semantic call for 'software'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# Geographic origin filter (assuming lang/country data is available)
response = requests.get('https://api.pulsebit.io/news_recent', params={'topic': 'software', 'language': 'en'})
data = response.json()

# Check for momentum
if data['momentum_24h'] < -0.5:
    print("Anomaly detected: Momentum spike of", data['momentum_24h'])

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

print("Meta-sentiment score:", sentiment_data['score'])

In the code above, we check the momentum for the software topic, specifically looking for values below -0.5. If we find such a value, we’ll trigger an alert. Then we run the narrative framing of the cluster reason back through the POST /sentiment endpoint. This is crucial because it allows us to evaluate how the sentiment itself shapes the story at play.

Now, let’s discuss three specific builds you can create using this anomaly detection pattern:

1. Geographic Filter Signal: Create a function that automatically queries software sentiment data by language, focusing on English-speaking regions. This will help you monitor local sentiment shifts promptly.

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

1. Meta-Sentiment Analysis: Build a dashboard that displays meta-sentiment scores for various clusters. For example, you could visualize how narratives around "service, (saas), market, expected, reach." evolve over time, providing a deeper understanding of market sentiment.

2. Forming Themes Tracker: Set up a scheduled job that checks for new forming themes in the software sector, particularly around SaaS. With the link https://news, you can aggregate articles and assess how mainstream media picks up on these signals, comparing it against current momentum scores.

We invite you to explore these features further in our documentation at pulsebit.lojenterprise.com/docs. With just a few lines of code, you can start detecting anomalies like the one we just uncovered in under 10 minutes. This could be a game-changer for your sentiment analysis projects.