Which term is commonly associated with removing common words that may not add significant meaning in text analysis?

The term associated with removing common words that may not contribute significant meaning in text analysis is "stop words." In natural language processing (NLP), stop words are typically defined as words such as "is," "the," "and," and similar terms that appear frequently in a language but often do not carry substantial meaning in the context of analysis. By filtering out stop words, data scientists and analysts can focus on the more meaningful words in a text, which can enhance the performance of various text mining and machine learning algorithms.

This removal process is particularly important in tasks like topic modeling, sentiment analysis, or when creating word embeddings, as it reduces the dimensionality of the dataset and allows for a clearer understanding of the underlying data patterns.

The other terms listed have different functions in text processing: lemmatization involves reducing words to their base or dictionary form; tokenization is the process of breaking text into individual words or phrases; and stemming reduces words to their root forms. While all these processes are important in text analysis, they do not specifically refer to the filtering of common, less informative words like stop words do.