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

Sentiment analysis is crucial for understanding market trends, especially in the hardware sector. However, scraping sentiment data from various sources can be tedious and error-prone. Fortunately, the Pulsebit API provides a straightforward solution for accessing sentiment data with a single endpoint.

The Problem (DIY scraping pain)

As developers, we often find ourselves scraping multiple websites to gather sentiment data. This process can be cumbersome due to rate limits, CAPTCHAs, and constantly changing HTML structures. Additionally, combining data from various sources to create a clear picture of market sentiment can be a nightmare.

For instance, you might have to scrape several news articles, perform natural language processing, and then aggregate those results. This approach not only consumes valuable development time but also introduces potential inaccuracies.

The Solution (Pulsebit API — one endpoint)

The Pulsebit API simplifies sentiment analysis by providing a single endpoint that returns semantic analysis of news articles related to hardware. You can get comprehensive sentiment metrics without the hassle of scraping.

The endpoint you’ll be using is /news_semantic, which provides key insights such as hardware sentiment, momentum, clusters, and confidence levels.

The Code (Python GET /news_semantic)

To get started with the Pulsebit API, you’ll need to make a GET request to the /news_semantic endpoint. Below is a Python example using the requests library.

import requests

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

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

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

# Replace 'your_api_key' with your actual Pulsebit API key
api_key = 'your_api_key'
sentiment_data = get_hardware_sentiment(api_key)
print(sentiment_data)

Make sure to replace 'your_api_key' with your actual API key. This code fetches the current hardware sentiment data.

Reading the Response

The response from the API will look something like this:

{
    "hardware_sentiment": 0.00,
    "momentum": 0.85,
    "clusters": 0,
    "confidence": 0.87
}

Here’s a breakdown of each field:

• hardware_sentiment: This value indicates the overall sentiment; a score of 0.00 suggests neutral sentiment.
• momentum: This is a momentum indicator; a score of 0.85 indicates strong positive momentum.
• clusters: This indicates the number of distinct sentiment clusters identified in the news data; in this case, it’s 0, meaning no distinct clusters were detected.
• confidence: A confidence level of 0.87 suggests high reliability in the sentiment analysis.

Three Use Cases

1. Algo Alert: You can set up an algorithmic trading alert that triggers when the hardware sentiment crosses a certain threshold. For example, if the momentum exceeds 0.80, you might want to buy or sell.

2. Slack Bot: Integrate this API into a Slack bot to send real-time alerts to your team. When the hardware sentiment shifts significantly, your bot can notify the channel, keeping everyone updated.

3. Dashboard: Create a dashboard using a framework like Dash or Flask to visualize sentiment trends over time. You can periodically fetch the sentiment data and display it using charts.

Get Started

Want to dive deeper? Check out the Pulsebit API documentation for detailed information on endpoints and usage. The API is well-documented, and you can easily integrate it into your existing systems.

With this approach, you can eliminate the pain of DIY scraping and focus on building valuable trading strategies with accurate sentiment data. Happy coding!