Build a Sentiment Analysis Pipeline for Web Scraping
While sometimes text content is easily retrieved through a database query, other times it’s not so simple to extract. For instance, it’s notoriously difficult to retrieve text content from websites, especially when you don’t want to extract everything from a URL — just the text content of specific pages.
For example, if you want to derive the sentiment from specific pages on a website, you can easily spend hours finding an appropriate web scraper, and then weeks labeling data and training a model for sentiment analysis.
This code snippet shows how to use Algorithmia to grab all the links from a web page, extracts the text content from each URL, and then returns the sentiment of each page.
For the full blog post related to this recipe, see Building a Sentiment Analysis Pipeline for Web Scraping.
Install the Algorithmia client from PyPi:
pip install algorithmia
You’ll also need a free Algorithmia account, which includes 5,000 free credits a month – more than enough to get started with crawling, extracting, and analyzing web data.
Find this line in the script:
client = Algorithmia.client("YOUR_API_KEY")
and add in your API key.
How to Find the Sentiment Analysis of your URL
After putting in your own API key to the line above run it in your console environment: