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Topic modeling is a type of statistical model used in natural language processing (NLP) to discover the abstract “topics” that occur in a collection of documents. One of the most common techniques for topic modeling is Latent Dirichlet Allocation (LDA). Here’s a step-by-step example of topic modeling using the gensim library in Python: Table of Contents Toggle 1. Import necessary libraries:2. Prepare the data:3. Preprocess the data:4. Create a dictionary and corpus:5. Build the LDA

Table of Contents Toggle Example 1: Removing URLsExample 2: Removing hashtags and mentionsExample 3: Removing special characters and punctuationExample 4: Extracting email addressesExample 5: Splitting text into sentencesExample 6: Finding phone numbersExample 1: Matching words starting with a specific letterExample 2: Extracting numbers from a stringExample 3: Removing consecutive duplicate wordsExample 4: Replacing multiple spaces with a single spaceExample 5: Extracting domain names from URLsExample 6: Splitting text using a delimiter Example 1: Removing URLs