Summary

NLP is becoming more and more important in AI. Industries analyze huge quantities of raw text data, which is unstructured. To understand this data, we use many libraries to process it. NLP is divided into two groups of methods and functions: NLG to generate natural language, and NLU to understand it.

Firstly, it is important to clean text data, since there will be a lot of useless, irrelevant information. Once the data is ready to be processed, through a mathematical algorithm such as TF-IDF or LSA, a huge set of documents can be understood. Libraries such as NLTK and spaCy are useful for doing this task. They provide methods to remove the noise in data. A document can be represented as a matrix. First, TF-IDF can give a global representation of a document, but when a corpus is big, the better option is to perform dimensionality reduction with LSA and SVD. scikit-learn provides algorithms for processing documents, but if documents are not pre-processed, the result will not be accurate...