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Transcribed image text: 1 import nltk 2 from import SentimentIntensityAnalyzer 3 import praw 4 5 reddit-praw.Reddit(client_id-'copy and paste your client id here', clientsecret-' copy and paste your secret here', user_agent- 'my user agent' 6 8 9 10 11 nltk.download( 'vader_lexicon') 12 sid-SentimentIntensityAnalyzer() 13 14 15 def get_text_negative_proba(text): 16 17 18 19 def get_text_neutral_proba (text): 20 21 return sid.polarity_scores (text) return sid.polarity_scores (text) submission- reddit.submission (url-url) _more() return ments 34 def main(): 35 36 37 38 39 40 comments = get-submission-comments(' comments/5W50g5/e115-what-is-recursion/') print(comments.body) print () neg -get_text_negative_proba(comments.replies. Show transcribed image text Expert Answer
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Implement process_comments(use recursion) and implement multiple tests for your method. You think this is a perfect opportunity to show off your recursion skills! In other words, you need to traverse all of the replies in the submission,which are structured in a tree-like format. The purpose of this method is to traverse the replies tree of a given submission,and return three lists: negative_comments_list, neutral_comments_list and positive_comments_list. View which stocks have been most impacted by COVID-19. Chegg is an America online textbook rental company based is California, that specializes in online textbook rentals, homework help, online tutoring. Since then, CHGG shares have decreased by 32.0 and is now trading at 24.99. She asks if you can implement the process_comments method that she left blank. Chegg's stock was trading at 36.74 on March 11th, 2020 when Coronavirus (COVID-19) reached pandemic status according to the World Health Organization.
#SELL CHEGG REDDIT CODE#
She writes a method called get_submission_comments that receives the URL of a Reddit submission as input (check the source code to see an example) and returns all the submission’s replies. She also writes the code to communicate with Reddit programmatically using their API. She also implemented two other methods: get_text_neutral_prob and get_text_positive_proba that do the same thing as get_text_negative_proba, but for neutral and positive sentiments. The closer the number is to 1, the more likely it is to be a negative comment. She implements a method called get_text_negative_proba that receives a string containing the comment to be analyzed and outputs a number between 0 and 1. As a first step to tackle this NLP problem, one of your team members decides to build a machine learning model to classify comments. You really like Reddit, but you have noticed that some subreddits are plagued with negativecomments.Tired of seeing this, you and a group of friends decide to create an extension for your favorite web browser that automatically hides negative comments when you visit Reddit.