Data is behind every decision we make, so the science around it need not feel abstract. This explores where data science meets linguistics, and how it is applied in practice across multilingual research, production and optimisation.
It works through the building blocks first, setting out the key terms, from computational linguistics and NLP to machine translation and human post-editing, then moves into real applications: filtering and sorting large volumes of search and content data, cleaning and structuring training data, and running sentiment analysis on how audiences respond. Each example is honest about the limits. Automated methods are never perfect, English is far better resourced than other languages, and a human in the loop remains the only route to near-complete accuracy.
The closing sections turn to measurement. Through incrementality testing and Performance Linguistics, it shows how content that was once judged on subjective opinion can be tested for real impact, using a worked example of SEO title-tag experiments that lifted sessions by 13%. The throughline is a balance of machine-driven and human-driven work: technology to handle scale and remove subjectivity, human expertise to provide the creativity, judgement and fine-tuning that data alone cannot.
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