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btc_features
btc_features
 
 
data
data
 
 
graphs
graphs
 
 
pic
pic
 
 
prices
prices
 
 
COMSE6998_Paper.pdf
COMSE6998_Paper.pdf
 
 
Feature_extract.ipynb
Feature_extract.ipynb
 
 
Feature_with_price.ipynb
Feature_with_price.ipynb
 
 
Features.ipynb
Features.ipynb
 
 
Final_feature_with_price.csv
Final_feature_with_price.csv
 
 
LSTM.ipynb
LSTM.ipynb
 
 
NLP_LSTM_priceinfo.ipynb
NLP_LSTM_priceinfo.ipynb
 
 
Price_label.ipynb
Price_label.ipynb
 
 
README.md
README.md
 
 
bitcoin market daily exploration.rmd
bitcoin market daily exploration.rmd
 
 
bitcoin_market_daily_exploration.html
bitcoin_market_daily_exploration.html
 
 
btc_price.csv
btc_price.csv
 
 
build features.py
build features.py
 
 
combined.txt
combined.txt
 
 
data_loader.py
data_loader.py
 
 
features_test.csv
features_test.csv
 
 
final_label.csv
final_label.csv
 
 
socialsent_Bitcoin.tsv
socialsent_Bitcoin.tsv
 
 
socialsent_Bitcoin.txt
socialsent_Bitcoin.txt
 
 
vader socialsent comparison.py
vader socialsent comparison.py
 
 
vader_lexicon.txt
vader_lexicon.txt
 
 

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NLP in Context Computational Models of Social Meaning

Sentiment Analysis for Bitcoin Prices

Tools: Keras, Python

Procedures:

  • First Step: Collect comments from Reddit Community.
  • Second Step: Transform comments into sentiment scores using Lexicons like VADER and ScoialSent.
  • Third Step: Label the data into four categories, oversampling and LSTM for training.

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