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What’s the most efficient way to handle scraped data in multiple languages?
I use Googletrans or similar translation APIs to standardize scraped text into a single language, making analysis easier.
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7 Replies
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Language-detection libraries like langdetect help me identify and sort data by language before processing.
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Setting up separate workflows for different languages, especially for tokenization, ensures accurate data handling.
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Translating key phrases or headers first helps organize data into categories before translating full content.
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Encoding issues can arise with non-English characters, so I ensure all data is processed in UTF-8 for consistency.
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Storing original and translated data side by side allows for comparisons and helps with quality checks.
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For common languages, using predefined dictionaries or templates speeds up categorization, especially with product data.
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Combining translation and NLP libraries, like spaCy, enables me to analyze multilingual data without extensive preprocessing.
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