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import nltk
nltk.download('vader_lexicon')
from nltk.sentiment.vader import SentimentIntensityAnalyzer

positive_words = 'buy bull long support undervalued underpriced cheap upward rising trend moon rocket hold hodl breakout call beat support buying holding high profit stonks yolo'
negative_words = 'sell bear bubble bearish short overvalued overbought overpriced expensive downward falling sold sell low put miss resistance squeeze cover seller loss '
pos = {i: 5 for i in positive_words.split(" ")}
neg = {i: -5 for i in negative_words.split(" ")}
stock_lexicons = {**pos, **neg}
vadar_analyzer = SentimentIntensityAnalyzer()
vadar_analyzer.lexicon.update(stock_lexicons)

def process_headline(headline):
    return vadar_analyzer.polarity_scores(headline)['compound']

ts_df['sentiment_score'] = ts_df['headline'].apply(process_headline)


# Cell 2
ts_df.head(5)

# Cell 3
ts_df.sample(10)

# Cell 4
import seaborn as sns

sns.violinplot(ts_df)