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

The Problem: DIY Scraping Pain

If you've ever tried to scrape sentiment data from multiple sources, you know it's a headache. Constantly managing changes in HTML structures, writing custom parsers, and dealing with rate limits can drain your energy and resources. You want data that’s timely, reliable, and easy to integrate, but scraping often feels like you're reinventing the wheel.

Today, let’s talk about a surprisingly effective tool that eliminates much of this pain: the Pulsebit API. With just one endpoint, you can harness the pulse of tech sentiment without the hassle of scraping.

The Solution: Pulsebit API — One Endpoint

The Pulsebit API offers a straightforward solution for sentiment analysis. By querying the /news_semantic endpoint, you can get real-time insights into tech sentiment, making it easy to stay ahead of trends.

Let’s dive into the code. You’ll need to make a GET request to the Pulsebit API to fetch sentiment data. Here’s how:

import requests

# Define the API endpoint and parameters
url = "https://pulsebit.lojenterprise.com/api/news_semantic"
params = {
    'topic': 'tech',
    'region': 'us'
}

# Make the GET request
response = requests.get(url, params=params)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print(f"Error: {response.status_code}")

Reading the Response

When you hit the endpoint, you’ll receive a payload with several fields. Let’s break down what each field means, especially in light of the current sentiment data:

• sentiment_score: Currently at +0.35, this indicates a positive outlook on tech. Notably, this is a rise from previous averages, suggesting a budding optimism in the sector.
• momentum_24h: At +1.30, this is significant. It shows a sharp increase in sentiment momentum — a spike worth paying attention to.
• confidence: With a confidence level of 0.87, you can trust this data. It indicates that the sentiment shift is backed by a solid data foundation.
• semantic_clusters: Currently at 0, this means that while the sentiment is rising, there are no distinct clusters of related topics emerging yet.
• direction: The sentiment is labeled as "rising." This is your cue to engage.

This combination of a high momentum score and a rising sentiment score is particularly unusual. It suggests that something is shifting in the tech landscape, and you should take note.

Three Use Cases

1. Algo Alert: Set up an algorithm that triggers alerts when sentiment momentum exceeds a certain threshold. For instance, if momentum rises above +1.00, you could automate a notification to your team to investigate potential opportunities.

2. Slack Bot: Integrate this data into a Slack bot that pings your channel when significant sentiment changes occur. This keeps your team informed without the need for constant manual checks.
   

   def send_slack_message(message):
       webhook_url = 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
       requests.post(webhook_url, json={'text': message})

   if data['momentum_24h'] > 1.00:
       send_slack_message(f"Tech sentiment has spiked! Current score: {data['sentiment_score']}")

1. Dashboard: Create a dashboard that visualizes sentiment over time. Use libraries like Matplotlib or Dash to plot sentiment scores and momentum, giving you a clear view of trends at a glance.

Get Started

Ready to dive into the Pulsebit API? Check out the official documentation to get all the details on how to set it up and start pulling in data. This is your chance to leverage tech sentiment shifts effectively, without the scraping pain.

So, as you integrate this tool, keep an eye on those unusual sentiment spikes — they might just lead you to your next big discovery.