You are comparing two common text representation methods for an NLP system. One is a sparse term-based approach, and the other is a dense embedding approach that captures semantic similarity between words.
What are TF-IDF and Word2Vec, and when would you use them?
Understanding of TF-IDF as a sparse lexical representationUnderstanding of Word2Vec as a dense semantic embedding methodWhen to use each approach in text classification or retrievalHow tokenization and preprocessing affect both methods