Reddit Sentiment Analysis
# https://www.learndatasci.com/tutorials/sentiment-analysis-reddit-headlines-pythons-nltk/
from IPython import display
import math
from pprint import pprint
import pandas as pd
import numpy as np
import nltk
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='darkgrid', context='talk', palette='Dark2')
import squarify
nltk.download()
showing info https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/index.xml
True
from nltk.stem import PorterStemmer
word_stemmer = PorterStemmer()
word_stemmer.stem('writing')
'write'
word_stemmer.stem('eating')
'eat'
import praw
reddit = praw.Reddit(client_id='SpMmWlA9VNMbn-8fbkm2bA',
client_secret='Bqi7WpVLw0qNimGAuVkXofKn1QuQ1g',
user_agent='owenAPI')
headlines = set()
for submission in reddit.subreddit('wallstreetbets').new(limit=None):
headlines.add(submission.title)
display.clear_output()
print(len(headlines))
913
from nltk.sentiment.vader import SentimentIntensityAnalyzer as SIA
sia = SIA()
results = []
for line in headlines:
pol_score = sia.polarity_scores(line)
pol_score['headline'] = line
results.append(pol_score)
pprint(results[:3], width=100)
[{'compound': 0.0,
'headline': 'Daily Discussion Thread for July 14, 2023',
'neg': 0.0,
'neu': 1.0,
'pos': 0.0},
{'compound': 0.0, 'headline': '$SAVE $35 Call Potential?', 'neg': 0.0, 'neu': 1.0, 'pos': 0.0},
{'compound': -0.7579,
'headline': 'World’s Empty Office Buildings Have Become a Debt Time Bomb',
'neg': 0.556,
'neu': 0.444,
'pos': 0.0}]
df = pd.DataFrame.from_records(results)
df.head(20)
| neg | neu | pos | compound | headline | |
|---|---|---|---|---|---|
| 0 | 0.000 | 1.000 | 0.000 | 0.0000 | Daily Discussion Thread for July 14, 2023 |
| 1 | 0.000 | 1.000 | 0.000 | 0.0000 | $SAVE $35 Call Potential? |
| 2 | 0.556 | 0.444 | 0.000 | -0.7579 | World’s Empty Office Buildings Have Become a D... |
| 3 | 0.000 | 1.000 | 0.000 | 0.0000 | Most Anticipated Earnings Releases for the wee... |
| 4 | 0.000 | 1.000 | 0.000 | 0.0000 | GOOGL: Breakout |
| 5 | 0.000 | 1.000 | 0.000 | 0.0000 | Tesla charging technology put on fast track to... |
| 6 | 0.000 | 0.714 | 0.286 | 0.3400 | BABA chart giving me that middle finger |
| 7 | 0.000 | 1.000 | 0.000 | 0.0000 | Did I do Call Options right? |
| 8 | 0.000 | 0.815 | 0.185 | 0.5267 | The world’s 500 richest people added $852bn to... |
| 9 | 0.092 | 0.704 | 0.204 | 0.4939 | $W Wayfair: significantly over-valued price an... |
| 10 | 0.000 | 1.000 | 0.000 | 0.0000 | Finally, somebody gets me |
| 11 | 0.000 | 0.638 | 0.362 | 0.4278 | Weed stocks are oversold. Small YOLO |
| 12 | 0.000 | 1.000 | 0.000 | 0.0000 | Average r/WallStreetBets user |
| 13 | 0.000 | 1.000 | 0.000 | 0.0000 | F NVDA, F RH , F market maker, FML |
| 14 | 0.161 | 0.839 | 0.000 | -0.3182 | Bank of America nurses $100bn paper loss after... |
| 15 | 0.000 | 0.270 | 0.730 | 0.4019 | Someone help. |
| 16 | 0.000 | 1.000 | 0.000 | 0.0000 | Daily Discussion Thread for June 28, 2023 |
| 17 | 0.000 | 1.000 | 0.000 | 0.0000 | What Are Your Moves Tomorrow, June 27, 2023 |
| 18 | 0.196 | 0.804 | 0.000 | -0.2960 | Why Slower Inflation Won't Prompt the Fed to L... |
| 19 | 0.000 | 0.833 | 0.167 | 0.3400 | I gave you $SAVE. My next play is $PENN, and m... |
df['label'] = 0
df.loc[df['compound'] > 0.2, 'label'] = 1
df.loc[df['compound'] < -0.2, 'label'] = -1
df.head()
| neg | neu | pos | compound | headline | label | |
|---|---|---|---|---|---|---|
| 0 | 0.000 | 1.000 | 0.0 | 0.0000 | Daily Discussion Thread for July 14, 2023 | 0 |
| 1 | 0.000 | 1.000 | 0.0 | 0.0000 | $SAVE $35 Call Potential? | 0 |
| 2 | 0.556 | 0.444 | 0.0 | -0.7579 | World’s Empty Office Buildings Have Become a D... | -1 |
| 3 | 0.000 | 1.000 | 0.0 | 0.0000 | Most Anticipated Earnings Releases for the wee... | 0 |
| 4 | 0.000 | 1.000 | 0.0 | 0.0000 | GOOGL: Breakout | 0 |
df2 = df[['headline', 'label']]
# df2.to_csv('reddit_headlines_labels.csv', mode='a', encoding='utf-8', index=False)
print("Positive headlines:\n")
pprint(list(df[df['label'] == 1].headline)[:10], width=200)
print("\nNegative headlines:\n")
pprint(list(df[df['label'] == -1].headline)[:10], width=200)
Positive headlines:
['BABA chart giving me that middle finger',
'The world’s 500 richest people added $852bn to their fortunes in the first half of 2023.',
'$W Wayfair: significantly over-valued price and ready to dump to 30 (or feel free to inverse me and watch to jump to 300).',
'Weed stocks are oversold. Small YOLO',
'Someone help.',
'I gave you $SAVE. My next play is $PENN, and my hedge is $GOOGL.',
'Someone Get This Guy the Nobel Prize in Economics',
'Interest rates, inflation and yield curve inversion. Part I.',
"How spread ask can be more than the spread's value?",
'Vuzix Receives OEM Order from Client Enabling Imaging Technology Solutions for Government, Defense and Commercial Markets']
Negative headlines:
['World’s Empty Office Buildings Have Become a Debt Time Bomb',
'Bank of America nurses $100bn paper loss after big bet in bond market',
"Why Slower Inflation Won't Prompt the Fed to Lower Rates",
'No seriously, what in the world is going on here?',
'Investors are gripped by fears of a recession, leading to a decline in global markets',
'EUR/USD: Uncertain Technical Outlook Ahead of US Jobs-Focused Week',
"Yolo'd some puts for an ER dump",
'The Downside of the Tendy Lifestyle: Liver Failure',
'Twitter is threatening to sue Meta over Threads',
'Can someone explain why I’m losing money']
print(df.label.value_counts())
print(df.label.value_counts(normalize=True) * 100)
0 562
1 231
-1 120
Name: label, dtype: int64
0 61.555312
1 25.301205
-1 13.143483
Name: label, dtype: float64
fig, ax = plt.subplots(figsize=(8, 8))
counts = df.label.value_counts(normalize=True) * 100
sns.barplot(x=counts.index, y=counts, ax=ax)
ax.set_xticklabels(['Negative', 'Neutral', 'Positive'])
ax.set_ylabel("Percentage")
plt.show()
from nltk.tokenize import word_tokenize, RegexpTokenizer
example = "This is an example sentence! However, it isn't a very informative one"
print(word_tokenize(example, language='english'))
['This', 'is', 'an', 'example', 'sentence', '!', 'However', ',', 'it', 'is', "n't", 'a', 'very', 'informative', 'one']
tokenizer = RegexpTokenizer(r'\w+')
tokenizer.tokenize(example)
['This',
'is',
'an',
'example',
'sentence',
'However',
'it',
'isn',
't',
'a',
'very',
'informative',
'one']
from nltk.corpus import stopwords
stop_words = stopwords.words('english')
print(stop_words[:20])
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his']
def process_text(headlines):
tokens = []
for line in headlines:
toks = tokenizer.tokenize(line)
toks = [t.lower() for t in toks if t.lower() not in stop_words]
tokens.extend(toks)
return tokens
pos_lines = list(df[df.label == 1].headline)
pos_tokens = process_text(pos_lines)
pos_freq = nltk.FreqDist(pos_tokens)
pos_freq.most_common(20)
[('stock', 49),
('stocks', 34),
('shares', 30),
('good', 26),
('best', 23),
('time', 18),
('earnings', 18),
('market', 17),
('help', 17),
('crypto', 17),
('3', 16),
('growth', 15),
('free', 14),
('ai', 13),
('interest', 12),
('tesla', 12),
('like', 12),
('new', 11),
('value', 11),
('5', 11)]
y_val = [x[1] for x in pos_freq.most_common()]
fig = plt.figure(figsize=(10,5))
plt.plot(y_val)
plt.xlabel("Words")
plt.ylabel("Frequency")
plt.title("Word Frequency Distribution (Positive)")
plt.show()
y_final = []
for i, k, z, t in zip(y_val[0::4], y_val[1::4], y_val[2::4], y_val[3::4]):
y_final.append(math.log(i + k + z + t))
x_val = [math.log(i + 1) for i in range(len(y_final))]
fig = plt.figure(figsize=(10,5))
plt.xlabel("Words (Log)")
plt.ylabel("Frequency (Log)")
plt.title("Word Frequency Distribution (Positive)")
plt.plot(x_val, y_final)
plt.show()
neg_lines = list(df[df.label == -1].headline)
neg_tokens = process_text(neg_lines)
neg_freq = nltk.FreqDist(neg_tokens)
neg_freq.most_common(20)
[('stocks', 17),
('stock', 14),
('recession', 11),
('us', 10),
('price', 8),
('market', 7),
('deal', 7),
('one', 6),
('earnings', 6),
('war', 6),
('short', 6),
('loss', 6),
('big', 6),
('debt', 6),
('gme', 5),
('still', 5),
('drop', 5),
('hold', 5),
('sell', 5),
('low', 5)]
y_val = [x[1] for x in neg_freq.most_common()]
fig = plt.figure(figsize=(10,5))
plt.plot(y_val)
plt.xlabel("Words")
plt.ylabel("Frequency")
plt.title("Word Frequency Distribution (Negative)")
plt.show()
y_final = []
for i, k, z in zip(y_val[0::3], y_val[1::3], y_val[2::3]):
if i + k + z == 0:
break
y_final.append(math.log(i + k + z))
x_val = [math.log(i+1) for i in range(len(y_final))]
fig = plt.figure(figsize=(10,5))
plt.xlabel("Words (Log)")
plt.ylabel("Frequency (Log)")
plt.title("Word Frequency Distribution (Negative)")
plt.plot(x_val, y_final)
plt.show()
# https://medium.com/nerd-for-tech/wallstreetbets-sentiment-analysis-on-stock-prices-using-natural-language-processing-ed1e9e109a37
# Import Libraries
import pandas as pd
import praw
import time
import matplotlib.pyplot as plt
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from datetime import date
import io
import requests as r
tickers_dict = {'AMZN': 5, 'TSLA': 1, 'GOOG': 3, 'META': 3, 'KO': 10, 'PEP': 5, # amazon, tesla, google, meta, coke, pepsi
'BA': 5, 'XOM': 5, 'CVX': 4, 'UNH': 1, 'JNJ': 3, 'JPM': 3, # boeing, exxon mobil, chevron, united health, johnson&johnson, jp morgan
'BAC': 5, 'C': 5, 'SPG': 10, 'AAPL': 6, 'MSFT': 5, 'WMT': 6, # bank of america, citigroup, simon property group, apple, microsoft, walmart
'LMT': 2, 'PFE': 10, 'MMM': 3, 'CRWD': 3, 'WBD': 20, 'DIS': 8, # lockheed martin, pfizer, 3M, crowdstrike, warner bros, disney
'AIG': 5, 'BRK-B': 4, 'DDOG': 3, 'SLB': 16, 'SONY': 5, 'PLD': 5, # american international group, berkshire hathaway, datadog, schlumberger, sony, prologis
'INT': 16, 'AMD': 5, 'ISRG': 3, 'INTC': 5} # world fuel services, advanced micro devices, intuitive surgical, intel
data_link = 'https://www.dropbox.com/s/6kh2u0qotscww5n/component.csv?raw=1'
response = r.get(data_link)
content = io.StringIO(response.text)
data = pd.read_csv(content)
stocks_df = data['Symbol']
stocks = stocks_df.to_dict().values()
# Set program parameters
subs = ['wallstreetbets', 'stocks', 'investing', 'stockmarket'] # sub-reddit to search
post_flairs = {'Daily Discussion', 'Weekend Discussion', 'Discussion'} # posts flairs to search || None flair is automatically considered
goodAuth = {'AutoModerator'} # authors whom comments are allowed more than once
uniqueCmt = True # allow one comment per author per symbol
ignoreAuthP = {'example'} # authors to ignore for posts
ignoreAuthC = {'example'} # authors to ignore for comment
upvoteRatio = 0.70 # upvote ratio for post to be considered, 0.70 = 70%
ups = 20 # define # of upvotes, post is considered if upvotes exceed this #
limit = 10 # define the limit, comments 'replace more' limit
upvotes = 2 # define # of upvotes, comment is considered if upvotes exceed this #
picks = 30 # define # of picks here, prints as "Top ## picks are:"
picks_ayz = 30 # define # of picks for sentiment analysis
posts, count, c_analyzed, tickers, titles, a_comments = 0, 0, 0, {}, [], {}
cmt_auth = {}
stocks
dict_values(['AAPL', 'ABBV', 'ABT', 'ACN', 'ADBE', 'AIG', 'AMD', 'AMGN', 'AMT', 'AMZN', 'AVGO', 'AXP', 'BA', 'BAC', 'BK', 'BKNG', 'BLK', 'BMY', 'BRK-B', 'C', 'CHTR', 'CL', 'CMCSA', 'COF', 'COP', 'COST', 'CRM', 'CSCO', 'CVS', 'CVX', 'DHR', 'DIS', 'DOW', 'DUK', 'EMR', 'EXC', 'F', 'FDX', 'GD', 'GE', 'GILD', 'GM', 'GOOG', 'GOOGL', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'KHC', 'KO', 'LIN', 'LLY', 'LMT', 'LOW', 'MA', 'MCD', 'MDLZ', 'MDT', 'MET', 'META', 'MMM', 'MO', 'MRK', 'MS', 'MSFT', 'NEE', 'NFLX', 'NKE', 'NVDA', 'ORCL', 'PEP', 'PFE', 'PG', 'PM', 'PYPL', 'QCOM', 'RTX', 'SBUX', 'SCHW', 'SO', 'SPG', 'T', 'TGT', 'TMO', 'TMUS', 'TSLA', 'TXN', 'UNH', 'UNP', 'UPS', 'USB', 'V', 'VZ', 'WBA', 'WFC', 'WMT', 'XOM'])
# Includes common words and words used on wsb that are also stock names
blacklist = {'I', 'ELON', 'WSB', 'THE', 'A', 'ROPE', 'YOLO', 'TOS', 'CEO', 'DD', 'IT', 'OPEN', 'ATH', 'PM', 'IRS', 'FOR','DEC', 'BE', 'IMO', 'ALL', 'RH', 'EV', 'TOS', 'CFO', 'CTO', 'DD', 'BTFD', 'WSB', 'OK', 'PDT', 'RH', 'KYS', 'FD', 'TYS', 'US', 'USA', 'IT', 'ATH', 'RIP', 'BMW', 'GDP', 'OTM', 'ATM', 'ITM', 'IMO', 'LOL', 'AM', 'BE', 'PR', 'PRAY', 'PT', 'FBI', 'SEC', 'GOD', 'NOT', 'POS', 'FOMO', 'TL;DR', 'EDIT', 'STILL', 'WTF', 'RAW', 'PM', 'LMAO', 'LMFAO', 'ROFL', 'EZ', 'RED', 'BEZOS', 'TICK', 'IS', 'PM', 'LPT', 'GOAT', 'FL', 'CA', 'IL', 'MACD', 'HQ', 'OP', 'PS', 'AH', 'TL', 'JAN', 'FEB', 'JUL', 'AUG', 'SEP', 'SEPT', 'OCT', 'NOV', 'FDA', 'IV', 'ER', 'IPO', 'MILF', 'BUT', 'SSN', 'FIFA', 'USD', 'CPU', 'AT', 'GG', 'Mar'}
# Adding wsb/reddit flavor to vader to improve sentiment analysis, score: 4.0 to -4.0
new_words = {
'citron': -4.0,
'hidenburg': -4.0,
'moon': 4.0,
'highs': 2.0,
'mooning': 4.0,
'long': 2.0,
'short': -2.0,
'call': 4.0,
'calls': 4.0,
'put': -4.0,
'puts': -4.0,
'break': 2.0,
'tendie': 2.0,
'tendies': 2.0,
'town': 2.0,
'overvalued': -3.0,
'undervalued': 3.0,
'buy': 4.0,
'sell': -4.0,
'gone': -1.0,
'gtfo': -1.7,
'paper': -1.7,
'bullish': 3.7,
'bearish': -3.7,
'bagholder': -1.7,
'stonk': 1.9,
'green': 1.9,
'money': 1.2,
'print': 2.2,
'rocket': 2.2,
'bull': 2.9,
'bear': -2.9,
'pumping': -1.0,
'sus': -3.0,
'offering': -2.3,
'rip': -4.0,
'downgrade': -3.0,
'upgrade': 3.0,
'maintain': 1.0,
'pump': 1.9,
'hot': 1.5,
'drop': -2.5,
'rebound': 1.5,
'crack': 2.5,}
start_time = time.time()
for sub in subs:
subreddit = reddit.subreddit(sub)
hot_python = subreddit.hot() # sorting posts by hot
# Extracting comments, symbols from subreddit
for submission in hot_python:
flair = submission.link_flair_text
# Check if the author exists
if submission.author is not None:
author = submission.author.name
else:
author = "Unknown" # Or any other value or action based on your needs
# Checking: post upvote ratio # of upvotes, post flair, and author
if submission.upvote_ratio >= upvoteRatio and submission.ups > ups and (flair in post_flairs or flair is None) and author not in ignoreAuthP:
submission.comment_sort = 'new'
comments = submission.comments
titles.append(submission.title)
posts += 1
submission.comments.replace_more(limit = limit)
for comment in comments:
# try except for deleted account?
try:
auth = comment.author.name
except:
pass
c_analyzed += 1
# checking: comment upvotes and author
if comment.score > upvotes and auth not in ignoreAuthC:
split = comment.body.split(' ')
for word in split:
word = word.replace("$", "")
# upper = ticker, length of ticker <= 5, excluded words
if word.isupper() and len(word) <= 5 and word not in blacklist and word in stocks:
# unique comments, try/except for key errors
if uniqueCmt and auth not in goodAuth:
try:
if auth in cmt_auth[word]:
break
except:
pass
# counting tickers
if word in tickers:
tickers[word] += 1
a_comments[word].append(comment.body)
cmt_auth[word].append(auth)
count += 1
else:
tickers[word] = 1
cmt_auth[word] = [auth]
a_comments[word] = [comment.body]
count += 1
# sorts the dictionary
symbols = dict(sorted(tickers.items(), key=lambda item: item[1], reverse = True))
top_picks = list(symbols.keys())[0:picks]
time = (time.time() - start_time)
# print top picks
print("It took {t:.2f} seconds to analyze {c} comments in {p} posts in {s} subreddits.\n".format(t=time,
c=c_analyzed,
p=posts,
s=len(subs)))
print("Posts analyzed saved in titles")
# for i in titles: print(i) # prints the title of the posts analyzed
print(f"\n{picks} most mentioned picks: {top_picks}")
It took 70.90 seconds to analyze 4829 comments in 47 posts in 4 subreddits.
Posts analyzed saved in titles
30 most mentioned picks: ['NVDA', 'TSLA', 'V', 'UPS', 'AAPL', 'META', 'JPM', 'AMD', 'MSFT', 'AMZN', 'UNH', 'PYPL', 'GOOGL', 'NFLX', 'MO', 'CRM', 'DIS', 'QCOM', 'CVS', 'PEP', 'T', 'C', 'BAC', 'WMT', 'DOW']
times = []
top = []
for i in top_picks:
print(f"{i}: {symbols[i]}")
times.append(symbols[i])
top.append(f"{i}: {symbols[i]}")
# Applying sentiment analysis
scores, s = {}, {}
vader = SentimentIntensityAnalyzer()
# adding custom words from data.py
vader.lexicon.update(new_words)
picks_sentiment = list(symbols.keys())[0: picks_ayz]
for symbol in picks_sentiment:
stock_comments = a_comments[symbol]
for cmnt in stock_comments:
score = vader.polarity_scores(cmnt)
if symbol in s:
s[symbol][cmnt] = score
else:
s[symbol] = {cmnt: score}
if symbol in scores:
for key, _ in score.items():
scores[symbol][key] += score[key]
else:
scores[symbol] = score
# calculating averages
for key in score:
scores[symbol][key] = scores[symbol][key] / symbols[symbol]
scores[symbol][key] = "{pol:.3f}".format(pol=scores[symbol][key])
# Printing sentiment analysis
print(f"\nSentiment analysis of top {picks_ayz} picks:")
df = pd.DataFrame(scores)
df.index = ['Bearish', 'Neutral', 'Bullish', 'Total_Compound']
df = df.T
print(df)
NVDA: 96
TSLA: 18
V: 13
UPS: 11
AAPL: 9
META: 8
JPM: 7
AMD: 4
MSFT: 4
AMZN: 3
UNH: 2
PYPL: 2
GOOGL: 2
NFLX: 1
MO: 1
CRM: 1
DIS: 1
QCOM: 1
CVS: 1
PEP: 1
T: 1
C: 1
BAC: 1
WMT: 1
DOW: 1
Sentiment analysis of top 30 picks:
Bearish Neutral Bullish Total_Compound
NVDA 0.101 0.738 0.160 0.113
TSLA 0.104 0.780 0.117 0.012
V 0.213 0.597 0.036 -0.224
UPS 0.146 0.725 0.129 0.131
AAPL 0.075 0.799 0.126 0.151
META 0.029 0.932 0.038 0.146
JPM 0.031 0.613 0.356 0.319
AMD 0.069 0.794 0.137 -0.172
MSFT 0.029 0.725 0.246 0.622
AMZN 0.039 0.823 0.138 0.567
UNH 0.035 0.893 0.072 0.448
PYPL 0.086 0.746 0.168 -0.036
GOOGL 0.037 0.561 0.402 0.628
NFLX 0.000 1.000 0.000 0.000
MO 0.000 0.673 0.327 0.346
CRM 0.074 0.783 0.143 0.888
DIS 0.092 0.793 0.115 0.130
QCOM 0.000 0.742 0.258 0.791
CVS 0.060 0.744 0.197 0.815
PEP 0.000 0.894 0.106 0.382
T 0.096 0.876 0.028 -0.959
C 0.096 0.876 0.028 -0.959
BAC 0.082 0.753 0.165 0.970
WMT 0.093 0.681 0.225 0.776
DOW 0.000 0.688 0.312 0.871
pd.DataFrame(df)
| Bearish | Neutral | Bullish | Total_Compound | |
|---|---|---|---|---|
| NVDA | 0.101 | 0.738 | 0.160 | 0.113 |
| TSLA | 0.104 | 0.780 | 0.117 | 0.012 |
| V | 0.213 | 0.597 | 0.036 | -0.224 |
| UPS | 0.146 | 0.725 | 0.129 | 0.131 |
| AAPL | 0.075 | 0.799 | 0.126 | 0.151 |
| META | 0.029 | 0.932 | 0.038 | 0.146 |
| JPM | 0.031 | 0.613 | 0.356 | 0.319 |
| AMD | 0.069 | 0.794 | 0.137 | -0.172 |
| MSFT | 0.029 | 0.725 | 0.246 | 0.622 |
| AMZN | 0.039 | 0.823 | 0.138 | 0.567 |
| UNH | 0.035 | 0.893 | 0.072 | 0.448 |
| PYPL | 0.086 | 0.746 | 0.168 | -0.036 |
| GOOGL | 0.037 | 0.561 | 0.402 | 0.628 |
| NFLX | 0.000 | 1.000 | 0.000 | 0.000 |
| MO | 0.000 | 0.673 | 0.327 | 0.346 |
| CRM | 0.074 | 0.783 | 0.143 | 0.888 |
| DIS | 0.092 | 0.793 | 0.115 | 0.130 |
| QCOM | 0.000 | 0.742 | 0.258 | 0.791 |
| CVS | 0.060 | 0.744 | 0.197 | 0.815 |
| PEP | 0.000 | 0.894 | 0.106 | 0.382 |
| T | 0.096 | 0.876 | 0.028 | -0.959 |
| C | 0.096 | 0.876 | 0.028 | -0.959 |
| BAC | 0.082 | 0.753 | 0.165 | 0.970 |
| WMT | 0.093 | 0.681 | 0.225 | 0.776 |
| DOW | 0.000 | 0.688 | 0.312 | 0.871 |
# Sentiment analysis
df = df.astype(float)
colors = ['red', 'springgreen', 'forestgreen', 'coral']
df.plot(kind='bar', color=colors, figsize=(12,6), title=f"Sentiment analysis of top {picks_ayz} picks:")
plt.show()
