Latent Semantic Analysis (LSA) is a modeling technique that can be used to understand a given collection of documents. It also provides us with insights into the relationship between words in the documents, unravels the concealed structure in the document contents, and creates a group of suitable topics - each topic has information about the data variation that explains the context of the corpus. This modeling technique can come in handy in a variety of natural language processing or information retrieval tasks. LSA can filter out the noise features in the data and represent the data in a simpler form, and discover topics with high affinity.
The topics that are extracted from the collection of documents have the following properties:
The amount of similarity each topic has with each document in the corpus.
The amount of similarity each topic has with each term in the corpus.
It also provides a significance score that highlights the importance of the topic and the variance...
