What are the different topic modelling algorithms in Gensim

In this recipe, we will learn the different topic modeling algorithms such as LDA, LSI, HDP in detail. We will also learn the syntax of each of these models.

Recipe Objective: What are the different topic modelling algorithms in Gensim

We'll go through some of the most common topic modeling algorithms in this part. Because Gensim abstracts them so well, we'll concentrate on 'what' rather than 'how.'

Latent Dirichlet Allocation (LDA)
Today's most prevalent and widely used technique for topic modeling is latent Dirichlet allocation (LDA). It's the one used by Facebook researchers in their 2013 research paper.

Hands-On Approach to Topic Modelling in Python 

LDA's Features

1) Topic modelling technique based on probabilistic probabilities
LDA is a technique for topic modelling that is based on probabilistic assumptions. As previously stated, topic modelling assumes that each document in a collection of associated documents (which could include academic papers, newspaper articles, Facebook posts, Tweets, e-mails, and so on) has some combination of themes. The primary purpose of probabilistic topic modelling is to uncover the hidden topic structure in a set of related documents. In general, a topic structure contains the following three elements: a) Topics, b)Statistical distribution of topics among the documents c) Words across a document comprising the topic.

2) Work in an unsupervised manner.
LDA is a self-contained system that works without the need for supervision. This is because LDA employs conditional probabilities to uncover the hidden topic structure. It is assumed that the themes are dispersed over the collection of interconnected papers.

3) It's pretty simple to make in Gensim.
It is relatively simple to develop an LDA model in Gensim. We need to define the corpus, dictionary mapping, and the number of topics we want to use in our model.

LDA may run into a computationally difficult challenge if we have an enormous number of subjects and words.

#syntax of lda model
lda_model=models.LdaModel(corpus, id2word=dictionary, num_topics=100)

Latent Semantic Indexing (LSI)
Latent Semantic Indexing (LSI) is a topic modelling approach developed in Gensim with Latent Dirichlet Allocation (LDA). Latent Semantic Analysis is another name for it (LSA). Building up an LSI model is similar to setting up an LDA model.

LSI is an NLP approach that is particularly useful in distributional semantics. It examines the relationship between a group of papers and the terminology contained in those documents. Talking about how it works creates a matrix from a large chunk of text that comprises word counts per document.

The LSI model employs a mathematical approach known as singular value decomposition (SVD) to minimize the number of rows. It keeps the similarity structure among columns while reducing the number of rows. The rows in a matrix represent distinct words, whereas the columns represent each document.

#syntax of LSI model
lsi_model=models.LsiModel(corpus, id2word=dictionary, num_topics=100)

Hierarchical Dirichlet Process (HDP)
Topic models like LDA and LSI aid in summarizing and organizing enormous archives of texts that are impossible to evaluate by hand. Aside from LDA and LSI, HDP is another helpful topic model in Gensim (Hierarchical Dirichlet Process). It's essentially a mixed-membership model for unsupervised data processing, and HDP infers the number of topics from the data, unlike LDA.

#syntax of HDP model
hdp_model=models.HdpModel(corpus, id2word=dictionary

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Time Series Forecasting Project-Building ARIMA Model in Python
Build a time series ARIMA model in Python to forecast the use of arrival rate density to support staffing decisions at call centres.

Text Classification with Transformers-RoBERTa and XLNet Model
In this machine learning project, you will learn how to load, fine tune and evaluate various transformer models for text classification tasks.

PyCaret Project to Build and Deploy an ML App using Streamlit
In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.

Deep Learning Project for Text Detection in Images using Python
CV2 Text Detection Code for Images using Python -Build a CRNN deep learning model to predict the single-line text in a given image.

Build Time Series Models for Gaussian Processes in Python
Time Series Project - A hands-on approach to Gaussian Processes for Time Series Modelling in Python

OpenCV Project to Master Advanced Computer Vision Concepts
In this OpenCV project, you will learn to implement advanced computer vision concepts and algorithms in OpenCV library using Python.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification

Build a Churn Prediction Model using Ensemble Learning
Learn how to build ensemble machine learning models like Random Forest, Adaboost, and Gradient Boosting for Customer Churn Prediction using Python