Topic Modelling with PyCaret

Description

Topic modeling is a powerful technique that allows you to automatically identify the main topics present in a collection of text documents. It is widely used in various fields such as natural language processing, information retrieval, and digital humanities. With PyCaret, you can easily perform topic modeling on large datasets and uncover insights that would otherwise be difficult to discover.

Are you looking to learn more about Topic Modeling?

Our course on Topic Modelling using PyCaret is here to help.

Imagine, you are in charge of monitoring social media tweets about a high-potential product for your company. Imagine combing through thousands of reviews, coding them for their emotional charge, and categorizing them.

Now, topic modeling can help you do tasks like these remarkably quicker AND derive valuable insights from the exercise.

Topic Modelling with PyCaret

PyCaret is a low-code, open-source auto-machine learning library that makes the process of building and deploying machine learning models easy and efficient. With PyCaret, you can perform topic modeling with just a few lines of code, saving you time and effort.

You will learn how to use PyCaret to perform topic modeling on text data, including preprocessing, feature extraction, and model training.  Next, we discuss how to evaluate the performance of your models and interpret the results.

What will you learn in this course?

This course focuses on learning about Topic Modelling, with a specific emphasis on the Latent Dirichlet Allocation (LDA) algorithm.

The course covers the PyCaret workflow and highlights the significance of custom stop words. This is what you can expect to learn:

1. Topic Modelling with Latent Dirichlet Allocation (LDA): The course begins by introducing the concept of Topic Modelling, which automatically identifies latent topics within a collection of documents. The Latent Dirichlet Allocation (LDA) algorithm assumes documents are generated from a mixture of topics. You will learn how it discovers these latent topics based on word distributions.

   
   

2. The Steps in the PyCaret Workflow: Next, the course moves on to explore the steps involved in the PyCaret workflow. PyCaret is a Python library that simplifies the end-to-end machine learning process. You will understand how to utilize PyCaret to streamline the topic modeling workflow and perform tasks like data preprocessing, model training, hyperparameter tuning, and model evaluation.

   
   

3. Importance of Custom Stop Words: Custom stop words play a crucial role in topic modeling. The course emphasizes their significance and explains how they can be used to improve the quality of topic extraction.

   
   

4. Application to Financial News Dataset: To apply the concepts and techniques learned, the course utilizes a financial news dataset. Financial news often contains specific terminology and domain-specific jargon, making it a challenging dataset for topic modeling. By experimenting with different custom stop words, you gain insights into the dataset and improve the accuracy and relevance of the extracted topics.

   
   

5. Visual Exploration with Word Clouds: As part of the course, you will also learn how to use visual aids, such as word clouds, to gain a quick overview of the dataset and visually explore its content. A word cloud is a visual representation of text data, where the size of each word corresponds to its frequency or importance within the dataset.

Using word clouds, you can generate a visual summary of the most common words or phrases in the dataset. By analyzing the word cloud, you can identify the prominent themes, topics, or frequently occurring terms. This provides a high-level understanding of the dataset's content and helps in formulating initial insights or hypotheses.

Word clouds can be generated for individual topics extracted from the dataset. Each word cloud represents the most representative words associated with a specific topic.

By examining these word clouds, you can gain a visual understanding of the main themes within each topic and identify key terms that differentiate them.

Throughout the course, you will gain a comprehensive understanding of topic modeling using LDA, learn the practical implementation of the PyCaret workflow, and explore the importance of custom stop words in improving topic extraction.

By the end of the course, you will have the knowledge and skills to perform topic modeling with PyCaret on your own projects and your own datasets, whether you're working in the humanities or you work with tweet datasets and customer reviews.

Topic Modelling when combined with other ML and network analysis tools like community detection and sentiment analysis is so powerful that it can be used to inform new product development. You can also combine it with search insight tools like Ask the Public to derive specific insights into consumer trends.

Don't miss out on this opportunity to add Topic Modeling to your list of skill sets.

TAKE THIS COURSE