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We are using YouTube videos to analyze and predict tags associated with each video using Machine Learning techniques and find interesting trends by performing sentiment analysis using Natural Language Processing on the top 10 trending videos in YouTube using title, description and comments.

pengyangwu.github.io/cs1951a/
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images
images
 
 
javascripts
javascripts
 
 
stylesheets
stylesheets
 
 
webapp
webapp
 
 
Final_Project_Report.pdf
Final_Project_Report.pdf
 
 
Poster_Updated.pdf
Poster_Updated.pdf
 
 
README.md
README.md
 
 
accuracy_data.csv
accuracy_data.csv
 
 
accuracy_measure.ipynb
accuracy_measure.ipynb
 
 
actual_predict_tags.ipynb
actual_predict_tags.ipynb
 
 
actual_sentiment_analysis.ipynb
actual_sentiment_analysis.ipynb
 
 
blog1.html
blog1.html
 
 
blog2.html
blog2.html
 
 
blog3.html
blog3.html
 
 
blog4.html
blog4.html
 
 
blog5.html
blog5.html
 
 
clean.py
clean.py
 
 
commentsFile.csv
commentsFile.csv
 
 
condense.py
condense.py
 
 
condensedStats.csv
condensedStats.csv
 
 
english_condensedStats.csv
english_condensedStats.csv
 
 
features.py
features.py
 
 
final_predicted_data.csv
final_predicted_data.csv
 
 
final_proposal.html
final_proposal.html
 
 
grabber.py
grabber.py
 
 
image00.png
image00.png
 
 
image2.png
image2.png
 
 
index.html
index.html
 
 
mapreduce.py
mapreduce.py
 
 
midterm.html
midterm.html
 
 
mostCommonTags.csv
mostCommonTags.csv
 
 
new_predict_tags.ipynb
new_predict_tags.ipynb
 
 
new_sentiment_analysis.ipynb
new_sentiment_analysis.ipynb
 
 
oldCommentsFile.csv
oldCommentsFile.csv
 
 
predicted_data.csv
predicted_data.csv
 
 
predicted_data_sentiment_analysis.ipynb
predicted_data_sentiment_analysis.ipynb
 
 
preprocessed_data.csv
preprocessed_data.csv
 
 
preprocessing.ipynb
preprocessing.ipynb
 
 
raw.json
raw.json
 
 
remove_special_chars_and_foreign_lang.ipynb
remove_special_chars_and_foreign_lang.ipynb
 
 
sentiment_analysis_comments_per_day.ipynb
sentiment_analysis_comments_per_day.ipynb
 
 
sentiment_analysis_of_comments_over_time.csv
sentiment_analysis_of_comments_over_time.csv
 
 
sentiment_analysis_of_predicted_tags.csv
sentiment_analysis_of_predicted_tags.csv
 
 
sentiment_analysis_of_top50_videos.csv
sentiment_analysis_of_top50_videos.csv
 
 
separateComments.py
separateComments.py
 
 
separatedCommentsFile.csv
separatedCommentsFile.csv
 
 
shuffled_preprocessed_data.csv
shuffled_preprocessed_data.csv
 
 
syns.txt
syns.txt
 
 
tempList.txt
tempList.txt
 
 
temp_preprocessed_data.csv
temp_preprocessed_data.csv
 
 
test.json
test.json
 
 
viz1.html
viz1.html
 
 
viz2.html
viz2.html
 
 
viz3.html
viz3.html
 
 
viz4.html
viz4.html
 
 
viz5.html
viz5.html
 
 
without_duplicates_condensedStats.csv
without_duplicates_condensedStats.csv
 
 
without_duplicates_final_predicted_data.csv
without_duplicates_final_predicted_data.csv
 
 

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youtube_recommender_system

We are using YouTube videos to analyze and predict tags associated with each video using Machine Learning techniques and find interesting trends by performing sentiment analysis using Natural Language Processing on the top 10 trending videos in YouTube using title, description and comments.

This is the Data Science (CS1951a) Final Project Website [Click Here]

Front end

Front-end source code is here:

https://github.com/pengyangwu/CS1951a

Files: html/css/js

Back-end (Part 1): Data Grabbing by YouTube API

Back-end (Part 1) source code is here:

https://github.com/PrestonLaw/CSCI1951AProject/tree/master

Files: python

Back-end (Part 2): Prepocessing (Extraction & Cleaning), Machine Learning & Sentiment Analysis Model

Back-end (Part 2) source code is here:

https://github.com/abhishekdutta/youtube_tag_predictor_sentiment_analysis

Files: ipython notebook/python

About

We are using YouTube videos to analyze and predict tags associated with each video using Machine Learning techniques and find interesting trends by performing sentiment analysis using Natural Language Processing on the top 10 trending videos in YouTube using title, description and comments.

pengyangwu.github.io/CS1951a/

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