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

We recently stumbled upon a compelling anomaly: a 24-hour momentum spike of -0.450 in the software sector. This significant dip in momentum could indicate underlying changes in sentiment that warrant immediate attention. The accompanying cluster story, "Software as a Service (SaaS) Market Expected to Reach US$908.2 - openPR.com," adds another layer to this anomaly, especially since there are no articles yet reflecting this shift.

This situation raises a critical issue in any sentiment analysis pipeline. If your model doesn't account for multilingual origins or dominant entities, it risks missing out on valuable insights. In our case, a model that fails to capture this anomaly by even a few hours could leave you blind to shifts in the software landscape, particularly given the global nature of software discussions. In this instance, the lack of a geo filter could mean missing critical nuances in sentiment, especially when the dominant language might not be English.

en coverage led by 10.3 hours. id at T+10.3h. Confidence scores: en 0.87, es 0.85, fr 0.86 Source: Pulsebit /sentiment_by_lang.

To catch this anomaly, our Python code snippet demonstrates how to leverage our API effectively. Below is a straightforward implementation that captures the core sentiment data.

import requests

![Left: Python GET /news_semantic call for 'software'. Right: ](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773305285201.png)
*Left: Python GET /news_semantic call for 'software'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


topic = 'software'
score = +0.000
confidence = 0.87
momentum = -0.450

# 1. Geographic origin filter
# Note: Data unavailable for geo filter, but here's how it could look
def fetch_data_by_geo(lang='en', country='US'):
    response = requests.get(f'https://api.pulsebit/your_endpoint?topic={topic}&lang={lang}&country={country}')
    return response.json()  # You would process this data as needed

![[DATA UNAVAILABLE: countries — verify /news_recent is return](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_geo_output_1773305285285.png)
*[DATA UNAVAILABLE: countries — verify /news_recent is returning country/region values for topic: software]*


# 2. Meta-sentiment moment
meta_sentiment_input = "Clustered by shared themes: service, (saas), market, expected, reach."
def fetch_meta_sentiment(input_text):
    response = requests.post('https://api.pulsebit/sentiment', json={'text': input_text})
    return response.json()

geo_data = fetch_data_by_geo()
meta_sentiment_data = fetch_meta_sentiment(meta_sentiment_input)

print("Geo Data:", geo_data)
print("Meta Sentiment Data:", meta_sentiment_data)

In this code, we first attempt to fetch data filtered by geographic origin, which is essential for understanding multilingual sentiment. While we're noting that geo filter data is currently unavailable, it's critical for you to implement this when the data is accessible.

Next, we run the cluster reason string through our sentiment endpoint to score its narrative framing. This is a unique feature that allows us to assess the context around our detected anomaly, deepening our understanding of the sentiment dynamics at play.

Now, let's discuss three specific builds you can create with this newfound pattern:

1. Geo Filter Analysis: Adjust your query to include a geographic origin filter when data becomes available. For example, you could set lang='en' and country='US' to hone in on American sentiment trends regarding software.

2. Meta-Sentiment Alerts: Build an alert system that triggers whenever a significant negative meta sentiment score emerges from your cluster analysis, particularly focusing on keywords such as "service" and "market." This can help you promptly act on emerging trends.

3. Anomaly Visualization Dashboard: Use the data returned from geo filtering and meta sentiment scoring to create a real-time dashboard. Display trends for software sentiment over time, highlighting significant shifts and their narratives to understand their implications better.

To get started with our API and integrate this discovery into your projects, head over to pulsebit.lojenterprise.com/docs. You can copy-paste the provided code and run it in under 10 minutes, setting you up to catch anomalies just like this one.