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

The Problem

As developers working with film data, we often face the challenge of tracking sentiment changes in real-time. The DIY approach usually involves scraping multiple websites, which can be time-consuming and inefficient. Not only do you have to deal with different HTML structures, but you also need to manage rate limits, anti-scraping measures, and data normalization. This can quickly become a mess, and let’s face it—you didn’t become a developer to spend hours parsing HTML.

The Solution

Enter the Pulsebit API. This API provides a straightforward way to access sentiment data with a single endpoint. By leveraging its capabilities, you can retrieve sentiment metrics like momentum and confidence with minimal hassle. The endpoint we’ll focus on is /news_semantic, which gives you real-time sentiment analysis for various topics, including films.

The Code

To get started, you’ll need to install the requests library, if you haven't already:

pip install requests

Here’s a simple Python script to fetch film sentiment data using the Pulsebit API:

import requests

def get_film_sentiment(api_key):
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }
    params = {
        "topic": "film"
    }

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

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error: {response.status_code} - {response.text}")

if __name__ == "__main__":
    API_KEY = "your_api_key_here"
    sentiment_data = get_film_sentiment(API_KEY)
    print(sentiment_data)

Replace your_api_key_here with your actual API key from Pulsebit.

Reading the Response

When you call the /news_semantic endpoint, you’ll receive a JSON response that includes several fields. Here’s what each field means:

• film_sentiment: This shows the overall sentiment score for films, where positive values indicate positive sentiment, negative values indicate negative sentiment, and zero is neutral. In our case, the sentiment is +0.00, indicating a neutral stance.

• momentum: This is a measure of how quickly sentiment is changing. A momentum of +0.90 suggests a strong upward trend in positive sentiment, indicating that recent discussions about films have been increasingly favorable.

• clusters: This indicates the number of distinct sentiment groups found in the data. Here, 0 clusters suggests that the sentiment is uniform across the data set.

• confidence: This is a measure of how confident the API is in its sentiment analysis, on a scale from 0 to 1. A confidence level of 0.87 means the API is quite confident about its sentiment assessment.

Three Use Cases

1. Algo Alert: Set up an algorithmic alert system that triggers notifications when sentiment crosses a certain threshold. For example, if sentiment drops below -0.5, alert your team to investigate potential PR issues.

2. Slack Bot: Create a Slack bot that posts updates on film sentiment at regular intervals. This keeps the team informed about how public perception is shifting, allowing for quicker responses to trends.

3. Dashboard: Integrate the sentiment data into a dashboard using tools like Dash or Streamlit. Visualize sentiment trends over time, showing how movies are perceived as new reviews and news articles come in.

Get Started

To dive deeper into the Pulsebit API, check out their official documentation. You’ll find comprehensive information on authentication, available endpoints, and additional use cases that can enhance your applications.

In conclusion, the Pulsebit API offers a powerful solution for quickly accessing film sentiment data without the hassle of DIY scraping. By using just one endpoint, you can build sophisticated tools to track and respond to sentiment shifts effectively.