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Zakary Goyette
3 years ago
tldr: I will be sharing all the possible NLP features that you can extract from unstructured texts for using in downstream tasks. I also list the python libraries I prefer to use for computing these features.
Text data and analysis has been one of the biggest trends and leaps of the last decade, especially the second half. This can be easily seen by the advancement of algorithms used by the tech titans in FAANG, as well as hundreds of startups that are disrupting various industries at a breakneck speed. Few of these industries have been completely transformed, while others are inevitably in the process of it. An example from personal experience — there is a huge disruption in the marketing and advertisement sector where the big agencies(like Dentsu, WPP, Publicis) are seeing a significant change in the way of doing and executing things.
#text-classification #machine-learning #unsupervised-learning #nlp
NLP: All the Features. Every Feature That Can Be Extracted From the Text. I will list down in detail, the shallow as well as deep features, that one can use as signals for downstream tasks like classifications, insights, visualizations, and so forth.