Web scraping is a powerful technique for extracting data from websites, but it can be challenging when dealing with multilingual content. How can you scrape websites that have different languages, scripts, and formats? In this article, you will learn some of the best techniques for scraping multilingual websites, such as:
Before scraping a website, you need to know what language it is written in. You can use various methods to detect the language of a web page, such as using the HTML lang attribute, checking the URL structure, or applying natural language processing (NLP) tools. Detecting the language can help you choose the right scraper, parser, and translator for your task.
To detect languages on a multilingual website, leverage the lang attribute in HTML tags as a first pass. For example, I scraped a tourism site with English and Spanish content. The HTML had "lang=en" or "lang=es" set, allowing my scraper to target the right content. When that attribute isn't available, use the URL structure and paths to identify languages. As a fallback, utilize Python NLP libraries like Langdetect to algorithmically determine languages based on word and character analysis. Knowing the languages upfront ensures properly tuned scrapers.
Depending on the language and structure of a website, different scrapers may be needed for accessing and downloading data. Requests, a Python library for sending HTTP requests and handling cookies, headers, and proxies, is one example. BeautifulSoup is another Python library that can parse HTML and XML documents to extract data using tags, attributes, and selectors. Selenium is a tool for automating web browsers and interacting with dynamic and JavaScript-heavy websites. Scrapy is a framework for creating and running spiders that can crawl and scrape multiple websites.
For scrapping python libraries like scrapy and beautiful soup are very helpful to scrap data from any website you want.Selenium is also useful.
Select scrapers suited to target languages and structures. When scraping a multilingual newspaper site with complex JavaScript-generated content, I used Selenium rather than simple Requests which couldn't execute page scripts. For scraping PDF documents in various European languages, I leveraged Tika's language detection and text extraction capabilities. Invest time upfront finding the best open source packages. Leverage Unicode and UTF-8 compatibility to handle diverse character sets. Test crawlers against sample target pages to validate effectiveness before broad scraping.
Once you have scraped the data, you need to parse it and extract the relevant information. Parsing multilingual data can be tricky, as different encoding, formatting, and character sets may be encountered. To handle these challenges, Unicode and UTF-8 encoding should be used. Additionally, regular expressions and string methods can be employed to clean and manipulate the text data. For malformed HTML documents, lxml or html5lib can be used as parsers for BeautifulSoup. Additionally, Pandas or CSV modules can be used to read and write tabular data.
Assume diverse encoding and dialects when parsing multilingual data. Pandas has robust UTF-8 support built-in, automatically handling issues like currency symbols and hyphenation. For scraping aggregate sites like Wikipedia with structural differences across languages, use flexible parsers like Beautiful Soup to first isolate then extract language-specific content areas. Where possible, utilize flag metadata to prevent data loss in cleaning and manipulation. For example, retaining French punctuation informs translation nuances later.
If you want to analyze or use data in a different language than the original one, you may need to translate it. There are various tools and services available for this purpose, such as Google Translate, which can translate text, web pages, and documents between many languages. TextBlob is a Python library that can perform basic translation and sentiment analysis using Google Translate API. PyTorch is a framework that can build and train neural machine translation models using deep learning techniques.
Integrate translation APIs like Google Cloud Translation into data pipelines to automate flows across multilingual datasets. For large corpus translations I've configured batches streamed to Google Cloud servers leveraging format preservation, regional dialect targeting and custom glossaries for precision. Validate outputs by spot checking accuracy metrics from Google against random manual reviews by native speakers.
After translating the data, you need to validate it and ensure that it is accurate and consistent. To do this, you can check the spelling and grammar of the translated text using tools like LanguageTool or Grammarly. Additionally, you can compare the translated text with the original one using tools like Diffchecker or Google Docs. Moreover, you can evaluate the quality and fluency of the translation using metrics like BLEU, ROUGE, or METEOR.
Assess grammaticality and spelling of translated content with LanguageTool before analysis, catching issues like incorrect word gender assignment. Programmatically compare translated text against the original source using Levenshtein distance to quantify distortion. Prioritize manual reviews on poor matches. For East Asian languages utilize native linguists to perform qualitative evaluations. Finally, use qualitative analytics rather than strict similarity metrics - a perfect translation may not always best serve end goals.
Finally, you need to store the data in a suitable format and location for further analysis or use. You can do this by saving the data as CSV, JSON, or XML files on your local machine or cloud storage. Alternatively, you can load the data into a database like SQLite, MySQL, or MongoDB. Moreover, you can upload the data to a data warehouse like Google BigQuery, Amazon Redshift, or Snowflake.
Plan multilingual data storage up front for future integration needs. I design MongoDB schemas with language and locale fields added at ingest, enabling efficient querying. Store original non-Latin scripts rather than transliterations which lose information. Choose collations supporting variant character sorting. Containerize language-specific unstructured datasets for access flexibility. Overall, optimize storage for analytics across languages - don't silo or restrict yourself too early in the pipeline.