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

The Problem

As a developer working with sports sentiment analysis, you might have faced the tedious pain of DIY scraping. Collecting data from various sports news websites can be a headache. It requires constant monitoring, handling different HTML structures, and parsing through noise to extract meaningful insights. This is not only time-consuming but also prone to errors. You want a clean, reliable way to monitor sentiment shifts in sports, especially when you see unusual spikes or drops in sentiment.

The Solution

Enter the Pulsebit API. With its single endpoint for news sentiment analysis, you can easily fetch sentiment data without the hassle of scraping. Today, the Pulsebit API is showing a notable sports sentiment score of +0.38, alongside a strong momentum of +1.18. This is a significant spike, especially given the historical context where sentiment typically hovers around neutral levels. Such a sudden rise indicates a shift worth investigating further.

The Code

To get started, you can use the following Python code to make a GET request to the /news_semantic endpoint of the Pulsebit API. Make sure you have the requests library installed:

import requests

# Define the endpoint and parameters
url = "https://pulsebit.lojenterprise.com/api/news_semantic"
params = {
    "topic": "sports"
}

# 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("Error fetching data:", response.status_code)

Reading the Response

Once you make the request, you'll receive a JSON response containing several fields. Here's what each field means:

• sentiment_score: The overall sentiment for the topic, in this case, sports. A score of +0.375 indicates a slightly positive sentiment, but it's the rising momentum that’s crucial.
• momentum_24h: This shows the change in sentiment over the last 24 hours. A momentum of +1.183 suggests a strong upward trend, indicating that sentiment is not just positive but accelerating.
• confidence: At 0.870, this high confidence level indicates that the sentiment score is reliable and robust, making your insights actionable.
• sentiment_index_0_100: A score of 68.75 places current sentiment well above neutral, reinforcing the positive outlook.
• direction: "rising" confirms that the sentiment is on an upward trajectory.
• semantic_clusters: With 0 clusters, it suggests that the sentiment is broadly positive without significant segmentation in the data.
• region: "global" indicates that this sentiment is relevant across various regions, not limited to a specific locale.
• semantic_similarity_avg: A score of 0.253 shows the average similarity of articles, giving you a sense of how aligned sentiments are across sources.

Three Use Cases

1. Algo Alert: Set up an algorithm that triggers alerts when the momentum exceeds a certain threshold. Given today's +1.18 momentum, you could write a rule to notify you for any momentum above +1.0.

2. Slack Bot: Build a Slack bot to push real-time sentiment updates to your channel. Integrate it to automatically post when sentiment shifts above a certain score, like +0.35, ensuring you and your team stay informed.

3. Dashboard: Create a dashboard that visualizes sentiment trends over time. Use the data from Pulsebit to show historical sentiment alongside the current spike, allowing for quick assessments of how current events are influencing sentiment.

Get Started

Ready to dive deeper? Explore the full capabilities of the Pulsebit API by visiting their documentation at pulsebit.lojenterprise.com/docs. With a few lines of code, you can transform how you detect and react to sentiment shifts in sports, making your development process significantly more efficient.