This project implements Latent Dirichlet Allocation (LDA) for topic modeling on English text documents. The implementation extracts hidden topics from a collection of academic papers using unsupervised machine learning techniques.

• Data preprocessing and text cleaning
• Word cloud visualization
• LDA model training with configurable topic numbers
• Interactive topic visualization using pyLDAvis

• Python 3.x
• pandas
• gensim
• nltk
• pyLDAvis
• wordcloud
• matplotlib

1. Data Loading: Loads academic papers from CSV file
2. Text Preprocessing:
   • Removes punctuation and converts to lowercase
   • Tokenizes text using Gensim's simple_preprocess
   • Removes English stopwords
3. Visualization: Generates word cloud for exploratory data analysis
4. Dictionary & Corpus Creation:
   • Creates word-to-id mapping using Gensim Dictionary
   • Converts documents to bag-of-words representation
5. LDA Training: Trains multicore LDA model with 10 topics
6. Results: Displays topic keywords and interactive visualization

• Topic keywords for each discovered topic
• Interactive pyLDAvis visualization for topic exploration
• Word cloud showing frequent terms in the dataset

Run the Jupyter notebook Topic_Modelling_In_English_LDA.ipynb sequentially to execute the complete pipeline.