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We will analyze the customer reviews of Sephora by using web crawling and nltk.
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This program crawls web text on a Sephora review page and returns the review ID, rating score, first name and location of the reviewer, their review text, and how many people found their review to be helpful.

Required Libraries

Use the following pip commands in command prompt to install the necessary libraries.

pip3 install requests 
pip3 install lxml
pip3 install beautifulsoup4
pip3 install nltk

Todo List

Items are listed in order of importance.

  1. create a map that shows th review frequencies and ratings by states
  2. Filter out <strong> tag and people in array_helpful (maybe try to use replace())

Solved Issues

Recent fixes are listed first.

  1. Fixed error "index out of range." The first (oldest) review did not have a rating, so we will omit this 1 entity from our data set.
  2. Stop skipping over hidden paragraphs (when reviews are long and you have to click "see more", the crawler skips over these parts)
  3. Fix 5, 4, 3, 2, 1 bug at the beginning of rating (for some odd reason the first 5 ratings on every page come in as 5, 4, 3, 2, 1 but that is incorrect)
  4. Tokenzie words using nltk
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