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Machine Learning in eDiscovery Upstream: eDiscovery Trends

Machine Learning in eDiscovery Upstream

Sentiment analysis & auto classification are not new to eDiscovery, but Veritas discusses how to apply that machine learning in eDiscovery upstream!

The article (Machine Learning in eDiscovery Upstream: Sentiment Analysis & Classification, written by Irfan Shuttari and available here) discusses how in this era where the global data sphere is expected to reach 175 zettabytes by 2025, it has never been more important for organizations to understand their data as early as possible.

Two machine learning tools to leverage in eDiscovery further upstream are sentiment analysis and automated (auto) classification:

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So, how can you apply sentiment analysis, auto classification and other machine learning in eDiscovery upstream? Check out Irfan’s article here to find out. It’s only one more click! Getting started with eDiscovery when the case or project begins is too late! 😉

So, what do you think? Are you applying machine learning techniques like sentiment analysis and auto classification? If so, when? Please share any comments you might have or if you’d like to know more about a particular topic.

Disclosure: Veritas is an Educational Partner and sponsor of eDiscovery Today

Disclaimer: The views represented herein are exclusively the views of the author, and do not necessarily represent the views held by my employer or my clients. eDiscovery Today is made available solely for educational purposes to provide general information about general eDiscovery principles and not to provide specific legal advice applicable to any particular circumstance. eDiscovery Today should not be used as a substitute for competent legal advice from a lawyer you have retained and who has agreed to represent you.

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