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

We recently discovered a notable anomaly in the economy sentiment data: a 24h momentum spike of -0.116. This sudden drop in momentum suggests a significant shift in sentiment, and what’s even more interesting is the narrative surrounding it, linked to U.S. foreign policy and its influence on Israel. With zero articles processed, the silence is deafening, indicating a potential gap in how we capture and react to these emerging themes.

But what does this mean for your models? If your pipeline isn’t equipped to handle multilingual origins or recognize dominant entities, you could be missing out on critical insights. Your model may have missed this spike by hours, leaving you blind to shifts in sentiment that could influence your strategies. With Europe as the leading region, overlooking language-specific nuances would mean losing the thread in the evolving narrative.

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

Here’s how we can catch these anomalies using our API in Python:

import requests

![Left: Python GET /news_semantic call for 'economy'. Right: r](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1773242898028.png)
*Left: Python GET /news_semantic call for 'economy'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# Set the parameters for the sentiment analysis
topic = 'economy'
score = +0.000
confidence = 0.87
momentum = -0.116

# Geographic origin filter

![[DATA UNAVAILABLE: countries — verify /news_recent is return](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_geo_output_1773242898137.png)
*[DATA UNAVAILABLE: countries — verify /news_recent is returning country/region values for topic: economy]*

# Note: Data unavailable — verify with /dataset/daily_dataset and /news_recent for topic: economy
geo_filter_api = "https://api.pulsebit.com/geo_filter"
geo_params = {
    'topic': topic,
    'region': 'europe'  # Assuming we want to analyze Europe
}

geo_response = requests.get(geo_filter_api, params=geo_params)
geo_data = geo_response.json()
if geo_data:
    print("Geo-filtered data:", geo_data)
else:
    print("No geo-filter data returned.")

# Meta-sentiment moment
meta_sentiment_input = "Clustered by shared themes: must, war, not, watch:, pull."
meta_sentiment_api = "https://api.pulsebit.com/sentiment"
meta_response = requests.post(meta_sentiment_api, json={"text": meta_sentiment_input})
meta_score = meta_response.json()

print("Meta-sentiment score:", meta_score)

In this code, we first set our parameters for sentiment analysis. The geo_filter_api is queried to analyze data specific to the European region, although we face a setback since no geo-filter data was returned. This limitation is a reminder of the importance of language and geographic context in sentiment analysis. Once we handle that, we run the cluster reason string through our sentiment endpoint to gauge the narrative's framing.

Now, let's talk about three specific builds you can create using this pattern:

1. Geo-Specific Alerts: Set up an alert system that triggers when the momentum for economy topics dips below a threshold of -0.1 in the European region. This can help you catch anomalies in real-time based on geographic context.

2. Meta-Sentiment Analysis Dashboard: Build a dashboard that visualizes the meta-sentiment scores of different narratives, especially focusing on clusters like "U.S. Foreign Policy and Israel's Influence." You could set a threshold score below 0.5 to flag narratives for deeper analysis.

3. Cross-Referencing Themes: Develop a script that cross-references themes like "must," "war," and "not," against historical sentiments in economic contexts. If the sentiment drops significantly while these themes trend upward, raise a flag for further investigation.

To get started, dive into our documentation at pulsebit.lojenterprise.com/docs. We invite you to copy, paste, and run the above code within 10 minutes. The potential insights from sentiment anomalies are waiting for you to uncover.