Extracting Text from Webpages: Use Cases & Benefits
Tags: webscraping, crawl, seo, python
Published: 2023-11-14 14:42:07

## What is Text Extraction?

Text extraction deals with the extraction of text from documents, webpages, or images. It can be done manually by going through different pages and extracting the text, or it can be done automatically using various automation tools.

In this blog, we will discuss some practical scenarios and use cases, while focusing on text extraction from webpages and also will go through some of the approaches which can help extract text from webpages.

Use Cases of Text Extraction from Webpage

### **1. Web Scraping**

Gathering information from websites can be boring and time-consuming if done manually. But with web scraping powered by text extraction, individuals and businesses can automate the process of collecting data from websites in a more efficient way.

A good example for this use case could be an online retailer who is looking to gather pricing data of products sold by a bunch of competitors. Doing this manually for hundreds of products across multiple websites is something that would normally take days. With web scraping, all the product titles, descriptions, and prices can be extracted automatically in a matter of minutes. Now, the retailer can then analyze the competitive pricing data to adjust their own prices.

### **2. Content Analysis and Data Mining**

Text extraction can also enable content analysis at scale. For example, a food delivery company can scrape customer reviews from various review sites to identify common complaints, food preferences and other insights to improve their services and menu offerings.

### **4. Financial and Market Research**

Financial analysts use text extraction to gather earnings reports, news articles and stock filings to identify investment opportunities faster. For example, a fintech company receives a large number of transaction payment receipts in digital format. They can use text extraction to identify spending patterns and aggregate the categories where consumers are spending more money and share them insight of spend accordingly.

### **5. Search Engine Optimization (SEO)**

Text extraction is linked to search engine optimization (SEO). a good example is the detection of duplicate content. Text can be extracted from webpages and analyzed to detect duplicate content. Duplicate content is a common issue that affects many websites, as it can lead to a range of negative consequences such as decreased search engine rankings and decreased user engagement. Identifying and resolving [duplicate content issues](https://marketbrew.ai/avoiding-duplicate-content-seo) is therefore an important task for website owners and content managers.

Now that we have covered some of the use cases of text extraction, let's now explore the possible approaches and resources available for extracting text from webpages. In general these approaches are:

- Using Visual Tools
- Using Open Source Libraries
- Using Cloud APIs.

Obviously there is no right or wrong approach, it strongly depends on the use case, available resources, availability of developers etc. Each of the approaches comes with its own benefits and we will see some of them below.

## **Visual Tools**:

[Diffbot](https://www.diffbot.com/products/extract/): Diffbot utilizes computer vision and machine learning to extract structured data from webpages without needing scraping rules. It classifies pages into one of 20 types then uses a model trained for that type to identify key attributes and transform the website into clean, structured data like JSON or CSV ready for applications. This automated approach extracts data from pages with minimal configuration.

[Web Scrapper](https://chrome.google.com/webstore/detail/web-scraper-free-web-scra/jnhgnonknehpejjnehehllkliplmbmhn): Web Scraper is a free and easy-to-use web data extraction chrome extension. The advantage is extraction runs directly in the browser without needing any software installation.

![diffbot](https://assets.apyhub.com/blog/extract-text-from-webpage-usecase/second.png)

These tools are suitable for non-developers like marketing professionals who can identify and evaluate the content for specific campaigns. The only challenge is manual effort if you try to extract data from thousands of pages.

## **Open Source Libraries / Frameworks:**

Popular open-source libraries like:

1. [BeautifulSoup](https://pypi.org/project/beautifulsoup4/) : Beautiful Soup is a very known python library that extracts information from web pages. It offers great [documentation](https://beautiful-soup-4.readthedocs.io/en/latest/) for developers which is actively maintained by the community.
2. [Scrapy](https://scrapy.org/): Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It offers rich developer community support and has been used by more than [50+ projects](https://scrapy.org/companies/).

![scrapy](https://assets.apyhub.com/blog/extract-text-from-webpage-usecase/third.png)

Libraries are a programmatic way to parse through page contents that could be suited for medium scale projects, but they have some limitations depending on the tech stack. For example, there may be fewer robust libraries available for other programming languages. Libraries also need to be updated by their maintainers to stay compatible with new language versions and fixes.

This is where cloud APIs can help bridge the gap. Rather than relying solely on libraries, cloud APIs allow you to access powerful pre-built services over the network through simple API calls.

## **Cloud APIs:**

ApyHub's [Extract Text from Webpage](https://apyhub.com/utility/extractor-webpage-text) is a cloud-based API that handles text extraction seamlessly. Users can simply pass the target URL and get back extracted text. It offers ( Up to 2 Million API Calls) on a free plan. You can even use visual API Playground which lets you test the output beforehand.

Primary benefits of API-enabled extraction include the ability to programmatically control or trigger your extraction as well as the ability to crawl on the scale.

This is the right one for the ones who prefer a more user-friendly approach and don't require complex coding implementation or infrastructure setup. You can simply call the APIs even from the front end and it will work seamlessly.

![apyhub](https://assets.apyhub.com/blog/extract-text-from-webpage-usecase/fourth.png)

In short, developers can use libraries like BeautifulSoup and cloud APIs like ApyHub. Non- developers can use visual tools to extract text from web pages easily. The right approach depends on one's technical expertise and use case requirements.

## Conclusion

Text extraction from web pages has a wide range of practical use cases. It allows for efficient web scraping, content analysis, financial research, NLP, content aggregation, SEO optimization, and e-commerce applications. By utilizing text extraction techniques, businesses can gain valuable insights, automate data collection, and improve decision-making processes. Cloud APIs like [ApyHub](https://apyhub.com/utility/extractor-webpage-text) provide a simple way to get started with extracting text from webpages.

[Read more](https://apyhub.com/blog) from ApyHub