def analyze_text_sentiment(tweet, company):
# Instantiates a client
client = language.LanguageServiceClient()
document = types.Document(content=tweet.full_text,
type=enums.Document.Type.PLAIN_TEXT)
# Detects the sentiment of the text and sends to stock price function
sentiment = client.analyze_sentiment(document=document).document_sentiment
if sentiment.score > .85 or sentiment.score < -.25:
print('Text: {}'.format(tweet.full_text))
print('Sentiment: {}, {}'.format(sentiment.score, sentiment.magnitude))
print("Tweet Date: ", tweet.created_at)
stock.export_tweet_stock_correlations(tweet.created_at, company)
def test_stock_data1(capfd):
date = datetime(2020, 10, 12, 2, 5, 46)
response = " Open High ... Dividends Stock Splits\nDate ... \n2020-10-12 442.000000 445.850006 ... 0 0\n2020-10-12 441.385590 443.499786 ... 0 0\n2020-10-12 440.630005 443.700012 ... 0 0\n2020-10-12 443.290009 444.890015 ... 0 0\n2020-10-12 443.558807 448.440002 ... 0 0\n2020-10-12 448.140015 448.739990 ... 0 0\n2020-10-12 444.640015 444.700012 ... 0 0\n\n[7 rows x 7 columns]\n"
stock.export_tweet_stock_correlations(date, 'TSLA')
out, err = capfd.readouterr()
assert out == response