This project focuses on conducting topic modeling on a customer complaint dataset using the Latent Dirichlet Allocation (LDA) algorithm implemented with Gensim. The goal is to uncover meaningful topics within the dataset and provide insightful visualizations using pyLDAvis.
Customer complaints are valuable sources of feedback for businesses, and understanding the main topics within these complaints can be crucial for improving customer satisfaction and product/service quality. This project employs the LDA algorithm to perform topic modeling on a customer complaint dataset, providing a structured way to identify key topics and patterns in the complaints.
The dataset used for this project contains a collection of customer complaints. It is preprocessed in this notebook data_preparing.ipynb and ready for topic modeling. You can find the dataset here.
Latent Dirichlet Allocation (LDA) is a popular algorithm for topic modeling. It works by identifying latent topics within a collection of documents and assigning each document a distribution over these topics. In this project, we use Gensim, a powerful library for topic modeling, to implement LDA.
To gain a better understanding of the topics identified by LDA, we use pyLDAvis for visualization. This interactive visualization tool allows you to explore the topics and their relationships in a user-friendly manner.
This project is licensed under the MIT License.