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A NLP study to analyze the language use in social media; topic model visualization and diagnostics tools

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JST
JST
 
 
examples
examples
 
 
.DS_Store
.DS_Store
 
 
.Rhistory
.Rhistory
 
 
136598840398995_facebook_statuses.csv
136598840398995_facebook_statuses.csv
 
 
1gramsData.csv
1gramsData.csv
 
 
2gramsData.csv
2gramsData.csv
 
 
3gramsData.csv
3gramsData.csv
 
 
README.md
README.md
 
 
SA_shiny.Rmd
SA_shiny.Rmd
 
 
SentiWords_1.1.csv
SentiWords_1.1.csv
 
 
SentiWords_cleaned.csv
SentiWords_cleaned.csv
 
 
amazon-count.txt
amazon-count.txt
 
 
amazonReviewSnippets_GroundTruth.csv
amazonReviewSnippets_GroundTruth.csv
 
 
amazon_stars.csv
amazon_stars.csv
 
 
amazon_topicSens.csv
amazon_topicSens.csv
 
 
convert_dat_JST.py
convert_dat_JST.py
 
 
get_fb_posts_fb_group.py
get_fb_posts_fb_group.py
 
 
lexicon_cleanup.py
lexicon_cleanup.py
 
 
metoo-count.txt
metoo-count.txt
 
 
metoo_stars.csv
metoo_stars.csv
 
 
metoo_topicSens.csv
metoo_topicSens.csv
 
 
metoo_topic_1.csv
metoo_topic_1.csv
 
 
movieReviewSnippets_GroundTruth.csv
movieReviewSnippets_GroundTruth.csv
 
 
movies-count.txt
movies-count.txt
 
 
movies_stars.csv
movies_stars.csv
 
 
movies_topicSens.csv
movies_topicSens.csv
 
 
nGrams_compare.Rmd
nGrams_compare.Rmd
 
 
ngrams.py
ngrams.py
 
 
ngramsCompare.py
ngramsCompare.py
 
 
ngramsNLTK.py
ngramsNLTK.py
 
 
nyt-count.txt
nyt-count.txt
 
 
nytEditorialSnippets_GroundTruth.csv
nytEditorialSnippets_GroundTruth.csv
 
 
nyt_stars.csv
nyt_stars.csv
 
 
nyt_topicSens.csv
nyt_topicSens.csv
 
 
par_coord.Rmd
par_coord.Rmd
 
 
sa_amazon.Rmd
sa_amazon.Rmd
 
 
sa_movies.Rmd
sa_movies.Rmd
 
 
sa_nyt.Rmd
sa_nyt.Rmd
 
 
sa_tweets.Rmd
sa_tweets.Rmd
 
 
scores_amazon.csv
scores_amazon.csv
 
 
scores_metoo.csv
scores_metoo.csv
 
 
scores_movies.csv
scores_movies.csv
 
 
scores_nyt.csv
scores_nyt.csv
 
 
scores_tweets.csv
scores_tweets.csv
 
 
sentimentAnalysis.py
sentimentAnalysis.py
 
 
starOne.csv
starOne.csv
 
 
starOne.html
starOne.html
 
 
starOne.js
starOne.js
 
 
starPlot.html
starPlot.html
 
 
starPlot.js
starPlot.js
 
 
stopwords.txt
stopwords.txt
 
 
topic_sentiment.py
topic_sentiment.py
 
 
topic_wrangling.py
topic_wrangling.py
 
 
tweets_GroundTruth.csv
tweets_GroundTruth.csv
 
 
tweets_stars.csv
tweets_stars.csv
 
 
tweets_topicSens.csv
tweets_topicSens.csv
 
 
vader_sentiment_lexicon.csv
vader_sentiment_lexicon.csv
 
 
write_txt.py
write_txt.py
 
 

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Quantifying emotional content of Facebook public storytelling posts

This project makes use of Max Woolf's Facebook metadata scraping method via the Facebook API, which can be seen on this Github page (https://github.com/minimaxir/facebook-page-post-scraper). Please refer back to his repository if you plan on using his scraping method! Thanks!

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A NLP study to analyze the language use in social media; topic model visualization and diagnostics tools

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