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Pulsebit News Sentiment API
Pulsebit News Sentiment API

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Your Pipeline Is 25.9h Behind: Catching Economy Sentiment Leads with Pulsebit

#python #api #datascience #nlp

Your Pipeline Is 25.9h Behind: Catching Economy Sentiment Leads with Pulsebit

We recently discovered an intriguing anomaly: a 24h momentum spike of -0.437. This spike indicates a significant shift in sentiment surrounding the global economy, particularly in the context of war-related narratives. With the leading language being Spanish press with a 25.9-hour lead, it’s clear that our sentiment pipeline needs to adapt to keep up with emerging trends.

The Problem

If your sentiment analysis pipeline doesn’t handle multilingual origins or account for entity dominance, you might find yourself lagging behind. In this case, your model missed this critical insight by a staggering 25.9 hours, primarily driven by Spanish-language content that highlighted the economic impacts of the war. Such delays can lead to missed opportunities or misguided strategies, especially when timely insights are crucial for decision-making.

Spanish coverage led by 25.9 hours. No at T+25.9h. Confidenc
Spanish coverage led by 25.9 hours. No at T+25.9h. Confidence scores: Spanish 0.85, English 0.85, French 0.85 Source: Pulsebit /sentiment_by_lang.

The Code

To catch this momentum spike effectively, we need to query our API for Spanish-language articles related to the economy. Here’s how you can do that:

import requests

# Define the parameters
params = {
    "topic": "economy",
    "lang": "sp",
    "score": -0.077,
    "confidence": 0.85,
    "momentum": -0.437
}

# API call to fetch data
response = requests.get('https://yourapi.com/sentiment', params=params)

![Left: Python GET /news_semantic call for 'economy'. Right: r](https://pub-c3309ec893c24fb9ae292f229e1688a6.r2.dev/figures/g3_code_output_split_1774474756607.png)
*Left: Python GET /news_semantic call for 'economy'. Right: returned JSON response structure (clusters: 3). Source: Pulsebit /news_semantic.*


# Process the response
data = response.json()
print(data)
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Next, we can run the cluster narrative through our POST /sentiment endpoint to gauge its framing:

# Define the narrative to analyze
narrative = "Clustered by shared themes: economy, west, asia, conflict."

# API call for sentiment scoring of the narrative
response = requests.post('https://yourapi.com/sentiment', json={"text": narrative})

# Process the response
meta_sentiment = response.json()
print(meta_sentiment)
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This gives us insights not just into the articles but also into how the overarching narrative is perceived.

Three Builds Tonight

  1. Geo-Filtered Alerts: Set a signal threshold to trigger alerts when momentum drops below -0.4 in Spanish-language articles. This ensures you're aware of any significant shifts in sentiment driven by regional content.

  2. Meta-Sentiment Dashboard: Build a dashboard that visualizes sentiment changes of clustered narratives, especially for themes like "economy", "war", and "global". This can highlight discrepancies between mainstream narratives and emerging themes, allowing you to pivot quicker.

  3. Real-time Analysis: Implement a pipeline that continuously runs the narrative sentiment through our API every hour, focusing on themes like "economy(+0.00)", "war(+0.00)", and "global(+0.00)" versus mainstream tags. This allows for immediate adjustments to strategies based on evolving sentiments.

Get Started

You can dive into this today by visiting pulsebit.lojenterprise.com/docs. With our API, you can copy, paste, and run these examples in under 10 minutes. Don’t let your model fall behind; ensure you’re capturing the full spectrum of sentiment across languages and narratives.

Geographic detection output for economy. India leads with 10
Geographic detection output for economy. India leads with 10 articles and sentiment +0.01. Source: Pulsebit /news_recent geographic fields.

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