Random Projections Model Generation for Document Set

This function uses the corpora and models modules from the gensim library to create a random projections model for a set of documents. It first converts the documents into a dictionary representation using the Dictionary object and then into a Bag-of-Words (BoW) representation using the doc2bow method. After that, it trains a RandomProjectionsModel instance, which is used for dimensionality reduction and noise reduction.

Technology Stack : gensim library

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def random_vector_space_model(document):
    from gensim import corpora, models
    # Create a dictionary representation of the documents.
    dictionary = corpora.Dictionary(document)
    # Using the dictionary, convert documents to a Bag-of-Words (BoW) representation.
    corpus = [dictionary.doc2bow(doc) for doc in document]
    # Train a Random Projections model on the corpus.
    model = models.RandomProjectionsModel(corpus, num_topics=50)
    return model