This project showcases an end-to-end workflow for topic modeling and text analysis using a variety of machine learning and natural language processing techniques. The goal of this project is to extract meaningful topics from a collection of text documents, enabling insights, categorization, and understanding of the underlying themes in the data.

Key Highlights:

Data Preparation: The project begins with data preprocessing, including tasks like text cleaning, tokenization, and lemmatization. This step ensures that the text data is in a suitable format for analysis.

Exploratory Data Analysis (EDA): EDA is performed on the dataset to gain initial insights into the content, distribution, and characteristics of the text data. Visualization techniques are used to understand the dataset better.

Feature Engineering: In this phase, we create numerical representations of the text data, such as TF-IDF (Term Frequency-Inverse Document Frequency) vectors, which are crucial for subsequent modeling.

Topic Modeling: The core of the project involves implementing three different topic modeling techniques:

Latent Dirichlet Allocation (LDA): A probabilistic generative model that assigns topics to documents and words to topics. Non-Negative Matrix Factorization (NMF): A matrix factorization technique that identifies patterns in the data. Latent Semantic Analysis (LSA): A dimensionality reduction technique based on singular value decomposition. Hyperparameter Tuning: For each of the topic modeling methods, hyperparameter tuning is performed to find the best settings, resulting in more coherent and interpretable topics.

Evaluation: Coherence scores are used to evaluate the quality of the generated topics and to compare the performance of different models. Silhouette scores provide insights into the separation of topics.

Visualization: The project includes visualizations of topics using tools like PyLDAvis, which allow for an interactive exploration of topics and their associated words.

Model Interpretability: SHAP (SHapley Additive exPlanations) is used to explain the importance of features (terms) in the context of the NMF model.

GitHub Repository: The code and documentation for this project are hosted on GitHub, providing a detailed walkthrough of the entire workflow and enabling others to replicate the analysis on their own datasets.

By the end of this project, we achieve a comprehensive understanding of the underlying topics in the text data, making it a valuable resource for anyone interested in text analysis and topic modeling techniques.