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

You’ve probably noticed that the mobile sector has been experiencing a peculiar moment. As of the latest reading, the mobile sentiment score stands at a surprising +0.00 with a momentum of +1.30. What’s particularly striking is the sentiment confidence at 0.87. This indicates that despite the sentiment score itself being neutral, the underlying momentum suggests a rising trend. So, how can you tap into this shift effectively?

The Problem: DIY Scraping Pain

If you’ve ever tried scraping sentiment data yourself, you know it’s no walk in the park. You’re dealing with unreliable sources, constantly changing HTML structures, and the burden of maintaining your scrapers. Not to mention the ethical implications of scraping from certain platforms! It’s tedious and time-consuming, which is why you need a reliable alternative.

The Solution: Pulsebit API — One Endpoint

Enter the Pulsebit API. It offers a single endpoint that can deliver the sentiment data you need without the hassle. For our use case, we’ll focus on the /news_semantic endpoint, which provides real-time sentiment analysis. This means you can get structured sentiment data directly related to mobile trends without the headaches associated with scraping.

The Code: Python GET /news_semantic

To get started, you’ll need to make a GET request to the Pulsebit API. Here’s a quick setup using Python with the requests library:

import requests

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

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

# Check for successful response
if response.status_code == 200:
    data = response.json()
    print(data)
else:
    print("Error:", response.status_code)

Make sure you replace the URL with your actual Pulsebit API endpoint if it's different. This code snippet fetches sentiment data for the mobile sector in the US.

Reading the Response

Once you have the data, you’ll receive a payload that looks something like this:

{
    "TOPIC": "mobile",
    "momentum_24h": +1.300,
    "sentiment_score": +0.000,
    "confidence": 0.870,
    "sentiment_index_0_100": 67.5,
    "direction": "rising",
    "semantic_clusters": 0,
    "region": "us",
    "semantic_similarity_avg": 0.233
}

Here’s what each field means:

• TOPIC: The subject of interest, in this case, "mobile".
• momentum_24h: Indicates the rate of change in sentiment over the last 24 hours. A +1.30 suggests positive momentum.
• sentiment_score: A score reflecting the general sentiment, currently neutral at +0.00.
• confidence: Higher values indicate more reliable data. At 0.87, this is quite solid.
• sentiment_index_0_100: A normalized score for quick reference (67.5 here is noteworthy).
• direction: Indicates whether the sentiment is rising or falling. Here, it’s "rising".
• semantic_clusters: The number of theme clusters detected; zero means no distinct clusters identified.
• region: The geographical focus of the data, which is "us".
• semantic_similarity_avg: Average similarity of sentiment-related news articles, here it's 0.233.

Three Use Cases

1. Algo Alert: Set up an algorithm to trigger alerts whenever the sentiment score or momentum crosses certain thresholds. For instance, if momentum exceeds +1.0 while confidence stays above 0.85, you might want to take action.

2. Slack Bot: Create a Slack bot that sends you updates on sentiment changes. This could help you stay informed about important shifts in real-time without manually checking the data.

3. Dashboard: Use this data to power a dashboard visualizing the sentiment trend. It’s a great way to keep stakeholders informed and can help in decision-making.

Get Started

Ready to dive in? Check out the Pulsebit API documentation for more details on how to authenticate and explore additional endpoints. This is an excellent opportunity to leverage sentiment analysis in a streamlined manner!

By utilizing the Pulsebit API, you can avoid the pain of DIY scraping while harnessing valuable insights in the mobile sector that could influence your development strategies.