Words (like articles or prepositions) that are removed from text analysis to focus on more meaningful terms, often customized for specific content.
Hyperparameter tuning is the process of adjusting the configuration settings of an ML algorithm to optimize performance and improve the quality of the model’s output. These parameters are set prior to or during the training process, and are distinct from the parameters learned during training.
In LDA topic modeling, hyperparameters that can be tuned include the number of topics (K), the number of passes or iterations, and the alpha and beta values, which influence the resulting coherence score.
Sources & References
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Bag of Words
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Bigram
A sequence of two adjacent words.
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Embedding
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Entity
A representation of real-world objects (people, products, places, concepts) that hold value from an SEO…
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Entity Variables (EAV)
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