gensim python tutorial

Publicado el: 31 enero 2024
en el canal de: CodeMade

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Gensim is a powerful Python library for topic modeling and document similarity analysis. It is particularly well-suited for working with large text corpora and has implementations of popular algorithms such as Word2Vec, Doc2Vec, and Latent Dirichlet Allocation (LDA). In this tutorial, we will cover the basics of Gensim and provide code examples to help you get started.
Before we begin, make sure you have Gensim installed. You can install it using pip:
A corpus is a collection of text documents. In Gensim, a corpus is typically represented as a list of lists, where each inner list contains the tokens of a document. Tokens can be words, phrases, or any other unit of text.
A Gensim dictionary maps each unique word in the corpus to a unique integer ID. It is used to create a bag-of-words representation of the documents.
Gensim models are used for various tasks such as topic modeling, document similarity, and word embeddings. There are different types of models available, such as LDA for topic modeling and Word2Vec for word embeddings.
Let's start by creating a simple text corpus:
Now, let's create a Gensim dictionary for our corpus:
Use the dictionary to convert the tokenized documents into a bag-of-words representation:
Let's use Latent Dirichlet Allocation (LDA) for topic modeling:
Now, let's use Word2Vec to create word embeddings:

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