You are a website developer working on a web application that allows users to create online stores where they can sell their ownerships of items from various categories or genres. You want to design and develop an algorithm in Python to suggest similar products within the store based on search queries. The user should be able to input criteria for finding similar products such as category, color, type of item (e.g., "vintage" and "handbags"). The code you should use is: In [CityName], there are two people named "Lola". Lola and Lisa have different relationships with each other. Lola has been living in a relationship with Tom, who she met when they were both working as detectives at the CIA. It's also known that their boss had an affair with her sister-in-law . Lola is 29 years old (born on January 15th). Both of them are women and have different physical appearances from each other: Lorenz, who was born in [Country]. He works for a company named "Wallaby". It's also known that her parents had been married once but were not related to Lola. The following information is given about the three girls and their relationships: 1. Lisa (25 years old) was born in [Country] and has two sisters, Emma and Sophie, with whom she's never been close to a romantic relationship. She works for an organization called "Crayon & Sons" where Lola is also employed. You are now the developer working on this problem: In our project we are trying to extract all user comments from these two webpages(https://www.instagram.com/p/b9573846?__ar=12091 and https://facebook.com/p/e9lCZFQ, which have a lot of text written in them. The task is to use NLP techniques (like lemmatisation) for the extraction of product features from these webpages: - How can we apply natural language processing (NLP) techniques to extract meaningful data about each product?

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