sentiment_analysis_on_stocks.py

from urllib.request import urlopen, Request
from bs4 import BeautifulSoup
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import pandas as pd
import matplotlib.pyplot as plt

finviz_url = 'https://finviz.com/quote.ashx?t='
tickers=['AMZN' , 'AMD' , 'FB' , 'GS' , 'JPM']

news_tables={}
for ticker in tickers:
    url= finviz_url +ticker
    req= Request(url=url,headers={'user-agent':'my-app'})
    response = urlopen(req)



    html = BeautifulSoup(response, 'html')
    news_table=html.find(id='news-table')
    news_tables[ticker]=news_table


parsed_data=[]

for ticker,news_table in news_tables.items():

    for row in news_table.find_all('tr'):

        title=row.a.get_text()
        date_data=row.td.text.split(' ')

        if len(date_data)==1:
            time=date_data[0]
        else:
            date=date_data[0]
            time=date_data[1]

        parsed_data.append([ticker,date,time,title])


df = pd.DataFrame (parsed_data, columns=['ticker','date','time','title'])

vader=SentimentIntensityAnalyzer()

f=lambda title:vader.polarity_scores(title)['compound']
df['compound'] = df['title'].apply(f)
df['date']=pd.to_datetime(df.date).dt.date

plt.figure(figsize=(10,8))

mean_df=df.groupby(['ticker','date']).mean()
mean_df=mean_df.unstack()
mean_df=mean_df.xs('compound',axis="columns").transpose()
mean_df.plot(kind='bar')
plt.show()