How to Detect Mobile Sentiment Shifts with the Pulsebit API (Python)

#python #api #datascience #trading

The Problem

Building a news sentiment pipeline from scratch takes weeks.
Web scrapers break. NLP models drift. You end up maintaining infrastructure instead of shipping product.

The Solution

The Pulsebit News Sentiment API does the heavy lifting: it ingests thousands of articles per hour,
extracts entities, clusters narratives, and returns clean confidence-weighted scores — in a single JSON response.

Current Mobile reading (live from the API):

avg_sentiment: +0.00
momentum_24h: +1.30
cluster_count: 20
confidence: 0.87

The Code

import requests

resp = requests.get(
    "https://api.pulsebit.lojenterprise.com/news_semantic",
    headers={"X-API-Key": "YOUR_KEY"},
    params={"query": "mobile", "limit": 20},
)
data = resp.json()

Reading the Response

Field	Value	Meaning
`avg_sentiment`	`+0.00`	Confidence-weighted sentiment score (-1 to +1)
`momentum_24h`	`+1.30`	Direction and speed of sentiment change
`confidence`	`0.87`	Signal quality — above 0.80 is reliable
`cluster_count`	`20`	Distinct narrative threads in the news stream

Three Use Cases

1. Momentum Alert (8 lines)

if abs(data["momentum_24h"]) > 0.10:
    send_alert(topic="mobile", score=data["avg_sentiment"])

2. Slack Bot (20 lines)
Fire a Slack webhook when confidence > 0.85 and momentum spikes. Your trading desk gets pinged before the headline hits.

3. Research Dashboard
Poll 10+ topics every 5 minutes. Pulsebit handles the NLP layer — you build the UI.