LDA Topic Modeling and Frequency Analysis of Text

This function uses the gensim library to analyze text, create a Bag-of-Words (BoW) model, and train an LDA topic model to identify topics within the text. It then returns the most frequently occurring word in the text.

Technology Stack : gensim

Code Type : Function

Code Difficulty : Intermediate

                
                    
def random_word_frequency(text):
    from gensim.corpora import Dictionary
    from gensim.models import LdaModel

    # Create a dictionary representation of the documents.
    dictionary = Dictionary([text.split()])

    # Create a Bag-of-Words (BoW) representation of the documents.
    corpus = [dictionary.doc2bow(text.split())]

    # Train a LDA model on the corpus.
    lda_model = LdaModel(corpus, num_topics=2, id2word=dictionary, passes=10)

    # Print the topics found by the LDA model.
    print(lda_model.print_topics())

    # Return the most frequent word in the text.
    most_frequent_word = max(dictionary.token2id.items(), key=lambda x: x[1])[0]
    return most_frequent_word