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

The Problem (DIY scraping pain)

If you’ve ever tried to gauge the sentiment around films, you know it can be a tedious task. Manually scraping multiple websites for reviews and social media sentiment is not only time-consuming but also prone to errors. You might find yourself dealing with inconsistent data formats, rate limits, and anti-scraping measures.

This is where the Pulsebit API comes into play, offering a streamlined solution to gather sentiment data without the hassle of DIY scraping. It aggregates data from various sources, allowing you to focus on analysis rather than data collection.

The Solution (Pulsebit API — one endpoint)

The Pulsebit API provides a single endpoint, /news_semantic, that returns sentiment analysis data in a structured format. This API not only saves you time but also provides reliable data on sentiment trends, including metrics like film sentiment, momentum, clusters, and confidence levels.

With the current data:

• Film sentiment: +0.00
• Momentum: +0.90
• Clusters: 0
• Confidence: 0.87

These metrics give you a compact view of how the film is being perceived in real-time.

The Code (Python GET /news_semantic)

Here’s how to get started with the Pulsebit API using Python. First, ensure you have the requests library installed:

pip install requests

Next, you can use the following code snippet to make a GET request to the /news_semantic endpoint:

import requests

def get_film_sentiment(api_key):
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": f"Bearer {api_key}"
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error: {response.status_code}")
        return None

# Usage
api_key = 'YOUR_API_KEY'
sentiment_data = get_film_sentiment(api_key)
print(sentiment_data)

Make sure to replace 'YOUR_API_KEY' with your actual API key from Pulsebit.

Reading the Response

When you call this endpoint, you’ll receive a JSON response that contains several fields. Here’s a breakdown of the fields you might encounter:

• film_sentiment: The overall sentiment score for the film. Here, a score of +0.00 indicates neutral sentiment.
• momentum: Indicates the rate of change in sentiment. A momentum of +0.90 suggests a strong upward trend in positivity.
• clusters: Represents how many distinct groups of sentiment there are. In our case, 0 indicates no distinct groups were found.
• confidence: A measure of how confident the API is in its sentiment analysis, with 0.87 suggesting high reliability.

Three Use Cases

Here are three practical use cases where this API can be integrated into your workflow:

1. Algo Alert: Set up an algorithm that monitors sentiment shifts. For example, if sentiment drops below a certain threshold, trigger an alert to take action (like adjusting marketing strategies).

2. Slack Bot: Create a Slack bot that posts daily updates on film sentiment. Using the API data, your bot can inform your team about changes in sentiment, providing context for ongoing discussions.

3. Dashboard: Build a dashboard that visualizes sentiment trends over time. By regularly pulling data from the API, you can create graphs that represent sentiment shifts, allowing stakeholders to make data-driven decisions.

Get Started

To dive deeper into what the Pulsebit API can offer, check out the Pulsebit API documentation. It provides comprehensive details on all available endpoints and how to utilize them effectively.

In summary, using the Pulsebit API simplifies the process of detecting film sentiment shifts. With just a few lines of code, you can access reliable sentiment data, allowing you to focus on more strategic tasks. Happy coding!