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…
read moreExample 1: Removing URLs text_with_url = “Check out our website: www.example.com for more information” clean_text = re.sub(r’http\S+’, ”, text_with_url) print(clean_text) text_with_url: Contains the input text with a URL. re.sub(r’http\S+’, ”, text_with_url): This uses the re.sub() function to replace any sequence of non-whitespace…
read moreProblem Statement: You are a data scientist working for a social media analytics company. Your team is tasked with conducting sentiment analysis on a large dataset of social media posts to gauge public sentiment towards a particular product launch. The dataset contains…
read moreText preprocessing is a fundamental step in most natural language processing (NLP) tasks. It involves transforming raw text into a format that is more suitable for the task at hand, whether it’s information retrieval, text classification, sentiment analysis, etc. Here are some…
read moreText preprocessing is a crucial step in natural language processing (NLP) and machine learning projects that deal with text data. It involves cleaning and transforming raw text into a form that can be readily used by machine learning models or other downstream…
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