The goal of the comprehensive project Document Clustering and Theme Extraction is to provide users with the tools necessary to effectively organize and extract useful information from massive text datasets. Data loading, text preparation, TF-IDF (Term Frequency-Inverse Document Frequency) transformation, K-means clustering, and Latent Dirichlet Allocation (LDA) for topic modeling are some of the components that make up this project. Its major objective is to offer a methodical way to comprehend and organize unstructured text data.
- Supports data ingestion from diverse sources, including documents, articles, and social media posts.
- Handles data formats like CSV, text, and web scraping.
- Cleans and prepares data for further analysis.
- Tokenization divides text into individual words or tokens.
- Utilizes stemming to reduce words to their root form, enhancing text normalization.
- Removes stop words and special characters to improve data quality.
- Computes TF-IDF scores to measure word importance within documents.
- Generates a TF-IDF matrix representing the entire dataset.
- Identifies and ranks significant terms and keywords.
- Applies K-means clustering to TF-IDF-transformed data.
- Automatically groups documents into clusters based on content similarity.
- Enables users to explore related documents within clusters.
- Offers flexibility in choosing the number of clusters (K).
- Utilizes LDA for unsupervised topic modeling.
- Identifies latent topics within the dataset.
- Assigns documents to topics, allowing for topic-based analysis.
- Supports topic visualization and interpretation.
- Enhances data understanding and organization.
- Facilitates content recommendation and personalized content delivery.
- Enables trend analysis and information retrieval in large text datasets.
- Serves academic, business, and research purposes.