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Pulsebit News Sentiment API
Pulsebit News Sentiment API

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

#python #api #datascience #trading

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

The Problem (DIY scraping pain)

As developers, we know that gathering sentiment data manually can be a tedious and often unreliable process. Scraping news articles, filtering out noise, and analyzing sentiment is not only time-consuming but also prone to inaccuracies. You might have tried various libraries and APIs, but they often leave you in a data desert when it comes to real-time sentiment shifts.

Right now, mobile sentiment is sitting at a score of +0.35 with a momentum of +1.30. That's a significant uptick, especially when the historical baseline for mobile sentiment has been notably lower. This spike isn't just a random blip; it's indicative of growing optimism in the mobile space, and understanding it can provide you with a competitive edge.

The Solution (Pulsebit API — one endpoint)

Enter the Pulsebit API. This tool is a game-changer for developers looking to tap into sentiment analysis with minimal effort. With just one endpoint, you can retrieve real-time sentiment data, making it easy to integrate into your existing applications. No more scraping nightmares or unreliable sentiment analyses.

To get started, you'll want to make a GET request to the /news_semantic endpoint. This single call provides you with a treasure trove of sentiment metrics.

The Code (Python GET /news_semantic)

Here's how to pull the data using Python:

import requests

# Define your endpoint
endpoint = "https://pulsebit.lojenterprise.com/api/news_semantic?topic=mobile"

# Make the GET request
response = requests.get(endpoint)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print(f"Error: {response.status_code}")
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This straightforward code snippet leverages the requests library to fetch your sentiment data. Make sure you have the library installed (pip install requests).

Reading the Response

Now, let's break down the response you'll get from the API:

{
  "TOPIC": "mobile",
  "momentum_24h": +1.300,
  "sentiment_score": +0.350,
  "confidence": 0.870,
  "sentiment_index_0_100": 67.5,
  "direction": "rising",
  "semantic_clusters": 0,
  "region": "us",
  "semantic_similarity_avg": 0.218
}
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  • TOPIC: The subject matter you're analyzing (in this case, "mobile").
  • momentum_24h: A crucial metric showing the sentiment's momentum over the last 24 hours. A +1.300 indicates a strong upward trend.
  • sentiment_score: The actual score reflecting positive or negative sentiment, where +0.350 suggests a generally optimistic outlook.
  • confidence: At 0.870, this indicates high confidence in the sentiment assessment.
  • sentiment_index_0_100: A normalized score (67.5), showing that sentiment is leaning positively.
  • direction: The current trend, which is "rising," reinforcing the other metrics.
  • semantic_clusters: The number of distinct themes detected in the sentiment data. In this case, it's 0, indicating a lack of diverse opinion clusters.
  • region: The geographical focus of the data (US).
  • semantic_similarity_avg: A measure of how closely related the sentiments are, here at 0.218.

Three Use Cases

  1. Algo Alert: Set up an alert system that triggers when sentiment crosses a certain threshold. For example, if sentiment rises above +0.40, you might want to execute a trading strategy or notify your team.

  2. Slack Bot: Create a Slack bot that reports on real-time sentiment shifts. With the current data indicating a rising sentiment in mobile, your team can stay in the loop without manual checks.

  3. Dashboard: Integrate this data into a dashboard for quick reference. A simple visualization showing sentiment score trends can help you make informed decisions on the fly.

Get Started

Want to dive deeper? Check out the Pulsebit API documentation for more details on how to implement and utilize the API effectively.

By leveraging this API, you can automate sentiment analysis and focus on what truly matters: turning insights into action. Don't let outdated methods hold you back; it's time to harness real-time data for your next big project.

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