Clean Web Scraping Data Using clean-text in Python

Web scraping has evolved as an effective method for obtaining information from websites. It allows individuals and organizations to collect information for a variety of objectives, including market research, sentiment analysis, and data−driven decision−making. However, online scraping frequently produces unstructured and jumbled data that must be cleaned and preprocessed before it can be used efficiently. The clean−text module in Python provides a complete and efficient solution for cleaning web scraping data, allowing users to preprocess and extract important insights from their scraped data.

Clean−text is a robust Python package for text cleaning and preprocessing. It includes a variety of functions and methods for dealing with text−related chores such as eliminating unnecessary characters, normalizing text, removing stop words, and much more. By leveraging clean−text's features, you can ensure that your online scraping data is clean, consistent, and suitable for further analysis.

Installing the clean−text library is the first step. Run the following line in your terminal or command prompt to install clean−text using the pip package management:

pip install clean-text

Once installed, you can import the clean−text library into your Python script or notebook using the following import statement:

from cleantext import clean

Now, let's explore some common use cases of clean−text for cleaning web scraping data in more detail:

Removing HTML Tags

Web pages often contain HTML tags that are unnecessary for text analysis. These tags may include formatting elements, hyperlinks, or other markup. Clean−text provides a built−in function called clean_html() that efficiently removes HTML tags from your scraped data. By applying this function, you can focus solely on the web page's textual content. Here's an example:

Example

raw_text = "<p>Hello, <strong>world!</strong></p>"
cleaned_text = clean(raw_text, clean_html=True)
print(cleaned_text)

Output

"Hello, world!"

Removing Unwanted Characters

Web scraping data often contains special characters, emojis, or non−alphabetic symbols that are irrelevant to your analysis. These characters can introduce noise and affect the accuracy of your results. Clean−text allows you to remove unwanted characters using the clean() function. It employs regular expressions to identify and eliminate special characters, ensuring that your text remains clean and focused. Here's an example:

Example

raw_text = "This is a sentence with unwanted characters 🙅♀️❤️"
cleaned_text = clean(raw_text, clean_special_chars=True)
print(cleaned_text)

Output

"This is a sentence with unwanted characters"

Normalizing Text

Text normalization is crucial for ensuring consistency in your data. Clean−text provides a clean() function with a lowercase parameter that allows you to convert text to lowercase. This is particularly useful for standardizing your text and avoiding duplicates based on case sensitivity. Additionally, you can leverage other normalization techniques provided by clean−text, such as removing diacritics or converting text to ASCII representation. Here's an example:

Example

raw_text = "Hello, World!"
cleaned_text = clean(raw_text, lowercase=True)
print(cleaned_text) 

Output

"hello, world!"

Removing Stop Words

Stop words are commonly used words in a language that do not carry significant meaning for text analysis. These words, such as "the," "is," and "and," can be safely removed from your web scraping data to focus on more meaningful content. Clean−text includes a predefined list of stop words that you can easily remove using the remove_stopwords parameter in the clean() function. Here's an example:

Example

raw_text = "This is an example sentence with some stop words"
cleaned_text = clean(raw_text, remove_stopwords=True)
print(cleaned_text) 

Output

"example sentence stop words"

Removing Punctuation

Web scraping data can contain punctuation marks that are unnecessary for many text analysis tasks. Clean−text provides a clean() function with a clean_punctuation parameter that allows you to remove punctuation from your scraped data. This can be particularly useful when punctuation does not contribute to the analysis or when dealing with language models that handle punctuation differently. Here's an example:

Example

raw_text = "This sentence includes punctuation!"
cleaned_text = clean(raw_text, clean_punctuation=True)
print(cleaned_text)

Output

"This sentence includes punctuation"

Handling Contractions

Web scraping data often includes contractions such as "can't" or "won't." Clean−text offers a clean() function with a replace_with_contractions parameter that allows you to handle contractions by replacing them with their expanded forms. This can be valuable for maintaining consistency and avoiding ambiguity in your text data. Here's an example:

Example

raw_text = "I can't believe it!"
cleaned_text = clean(raw_text, replace_with_contractions=True)
print(cleaned_text)

Output

"I cannot believe it!"

Removing Non−Textual Elements

Web pages may contain non−textual elements such as images, scripts, or advertisements. When scraping data, it's often desirable to exclude these elements from your text analysis. Clean−text provides a clean() function with a clean_non_text parameter that allows you to remove non−textual elements from your scraped data, leaving only the textual content. Here's an example:

Example

raw_text = "This is <img src='image.jpg'> an example"
cleaned_text = clean(raw_text, clean_non_text=True)
print(cleaned_text)

Output

"This is an example"

Lemmatization and Stemming

Lemmatization and stemming are techniques used to reduce words to their base or root form. These techniques can help in reducing variations of words and achieving better text normalization. While clean−text does not include built−in lemmatization or stemming functions, it seamlessly integrates with popular Python libraries such as NLTK or spaCy, allowing you to incorporate lemmatization and stemming into your web scraping data cleaning pipeline.

These examples demonstrate the core functionalities of clean−text for cleaning web scraping data. However, the library offers many more features and options for advanced text cleaning. For instance, clean−text allows you to remove URLs, email addresses, or numeric digits from your data. It also provides support for handling multiple languages, enabling you to preprocess text data from diverse sources.

While utilizing clean−text for web scraping data cleaning, it is important to remember to respect the website's terms of service and ensure that you are scraping data ethically and responsibly. Always make sure you have permission to access and scrape the target website's data and be mindful of the impact on server resources.

Conclusion

As a robust Python module, clean−text offers a practical and adaptable method for cleaning web scraping data. You may quickly preprocess your scraped data, eliminate extraneous components, and guarantee the accuracy and consistency of your text data by utilizing its extensive functionalities. Utilize clean−text in your web scraping initiatives to maximize the value of your data analysis efforts. To ensure ethical online scraping, keep in mind to abide by best practices and ethical standards.