def benchmark(nb_crypto):
liste_polarity = []
liste_average_senti = []
liste_crypto = get_ordonate_crypto_list()
print(liste_crypto[:nb_crypto])
for crypto in liste_crypto[:nb_crypto]:
loc_file = tws.get_tweet_from_subject(10, "en", crypto, 20, 20, 20)
print(loc_file)
df = pd.read_csv("data/" + loc_file, sep="\\", names=['Content'])
clean = nlp.clean_df(df)
tweet_list = sent_analy.get_sentence_from_array(clean)
liste_average_senti.append(
sent_analy.get_sentiment_analyse(tweet_list, schema=False))
liste_polarity.append(get_signe(liste_average_senti[-1]))
list_norm_average_senti = normalize(liste_average_senti)
print_benchmark(list_norm_average_senti, liste_polarity)
fig = plt.figure(figsize=(nb_crypto, 5))
ax = fig.add_axes([0, 0, 1, 1])
ax.set_title('Bilan analyse sentimental pour ' + str(nb_crypto) +
" Crypto")
ax.set_ylabel('Scores (Achat si sup à zéro Vente si inf)')
crypto_sentiment = print_benchmark(list_norm_average_senti, liste_polarity)
labels = liste_crypto[:nb_crypto]
ax.bar(labels, crypto_sentiment)
plt.show()
def main():
loc_file = tws.get_tweet_from_subject(10,"en","bitcoin",20,20,20)
print("\n Nom du fichier :",loc_file,"\n")
df = pd.read_csv("data/" +loc_file, sep="\\", names=['Content'])
clean = nlp.clean_df(df)
tweet_list = sent_analy.get_sentence_from_array(clean)
sent_analy.get_sentiment_analyse(tweet_list)
## Benchmark crypto
cryp_ben.benchmark(nb_crypto=5)
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 4 11:45:05 2020
@author: Victor HENRIO
"""
import nlp
import tweet_scraping_scroll as tws
import sentiment_analysis as sent_analy
import pandas as pd
if __name__ == "__main__":
loc_file = tws.get_tweet_from_subject(10, "en", "bitcoin", 20, 20, 20)
print(loc_file)
df = pd.read_csv("data/" + loc_file, sep="\\", names=['Content'])
clean = nlp.clean_df(df)
tweet_list = sent_analy.get_sentence_from_array(clean)
sent_analy.get_sentiment_analyse(tweet_list)