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

We recently observed an intriguing anomaly in our sentiment data: a 24-hour momentum spike of +0.442 related to immigration. This spike is significant and raises questions about the underlying sentiment trends. It speaks volumes about the ongoing discussions and public sentiment surrounding immigration, especially considering the current socio-political climate in the U.S.

But what does this spike really mean? If your model isn't tuned to handle multilingual origins or account for dominant entities, you might have missed this shift by several hours. In this case, the dominant language is English, but there are often significant discussions in Spanish and other languages that can provide a fuller picture. Ignoring this linguistic diversity can leave your model blind to crucial sentiment changes, especially in a topic as nuanced as immigration.

![DATA UNAVAILABLE: lag_hours — verify /dataset/daily_dataset
[DATA UNAVAILABLE: lag_hours — verify /dataset/daily_dataset is returning sentiment_by_lang data for topic: immigration]

Let’s dive into how to catch such anomalies using our API. Below is the Python code to filter sentiment data for immigration, score the narrative, and analyze the results.

import requests

![Left: Python GET /news_semantic call for 'immigration'. Righ](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773100205903.png)
*Left: Python GET /news_semantic call for 'immigration'. Right: returned JSON response structure (clusters: 0). Source: Pulsebit /news_semantic.*


# Define parameters for the query
topic = 'immigration'
score = +0.000
confidence = 0.87
momentum = +0.442

# Example: Geographic origin filter (assuming geo data is available)
# This part of the code would only work if geo filter data was returned.
geo_filter = {
    'language': 'en',
    'country': 'us'
}

![[DATA UNAVAILABLE: countries — verify /news_recent is return](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_geo_output_1773100205985.png)
*[DATA UNAVAILABLE: countries — verify /news_recent is returning country/region values for topic: immigration]*


# Fetch sentiment data
response = requests.get(f'https://api.pulsebit.com/sentiment?topic={topic}&region={geo_filter["country"]}')
sentiment_data = response.json()  # Make sure to handle errors in production code

# If geo filter data is available, analyze
if sentiment_data:
    print(f"Sentiment data for {topic}: {sentiment_data}")

# Meta-sentiment moment: scoring the narrative framing itself
narrative = "Immigration narrative sentiment cluster analysis"
meta_response = requests.post('https://api.pulsebit.com/sentiment', json={'input': narrative})
meta_sentiment = meta_response.json()
print(f"Meta sentiment for narrative: {meta_sentiment}")

In this code, we first define our parameters, including the topic, score, confidence, and momentum. Next, we attempt to apply a geographic origin filter. It’s essential to highlight that this is only effective when the relevant language and country data are available. After querying the sentiment data, we analyze the output for insights.

Our second mandatory step is crucial. We run a meta-sentiment analysis on the narrative framing itself. This allows us to gauge how the narrative around immigration is being perceived, providing a deeper insight into the sentiment landscape.

Now that we've established how to detect anomalies, here are three specific builds you can create with this pattern:

1. Geographic Sentiment Dashboard:
   Build a real-time dashboard that uses a geo filter to track immigration sentiment across various regions. Set a threshold for momentum spikes above +0.4 to trigger alerts, ensuring you never miss significant shifts.

2. Meta-Sentiment Alerts:
   Create a system that sends you alerts whenever the meta-sentiment for narratives, like "Immigration narrative sentiment cluster analysis," deviates from the norm. Set a threshold for significant changes, say a 10% rise or drop in sentiment.

3. Comparative Analysis Tool:
   Develop a tool that compares the current sentiment on immigration with historical data. Use the metrics from the last 30 days as a baseline and flag any significant deviations, particularly those that coincide with news events or policy changes.

If you’re ready to dive into sentiment analysis, check out our documentation. With the right setup, you can copy-paste the code above and run it in under 10 minutes. This approach can significantly enhance your ability to detect and respond to sentiment anomalies effectively.