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Stock Sentiment Analysis using headlines – with source code – easiest explanation – 2023

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In today’s blog, we will see how we can perform stock sentiment analysis using the headlines of a newspaper. We will predict if the stock market will go up or down. This is a simple but very interesting project due to its prediction power, so without any further due, Let’s do it…

Step 1 – Importing libraries required for Stock Sentiment Analysis.

import pandas as pd
import pickle
import joblib
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix,accuracy_score

Step 2 – Reading input data.

df = pd.read_csv('data/Data.csv')

Step 3 – Cleaning our data.

headlines = []

cleaned_df = df.copy()

cleaned_df.replace('[^a-zA-Z]', ' ',regex=True,inplace=True)
cleaned_df.replace('[ ]+', ' ',regex=True,inplace=True)

for row in range(len(df)):
    headlines.append(' '.join(str(x) for x in cleaned_df.iloc[row,2:]).lower())

Step 4 – Initializing CountVectorizer.

cv = CountVectorizer(ngram_range=(2,2))

Step 5 – Checking our data.


Step 6 – Splitting data.

train_data = cleaned_df[df['Date']<'20150101']
test_data = cleaned_df[df['Date']>'20141231']

train_data_len = len(train_data)

train_headlines = cv.transform(headlines[:train_data_len])
test_headlines = cv.transform(headlines[train_data_len:])

Step 7 – Training our model.

rfc = RandomForestClassifier(n_estimators=200,criterion='entropy'),train_data['Label'])

preds = rfc.predict(test_headlines)


Step 8 – Saving our model.

joblib.dump(rfc, 'stock_sentiment.pkl')

Download Source code and Data for Stock Sentiment Analysis…

Do let me know if there’s any query regarding Stock Sentiment Analysis by contacting me by email or LinkedIn.

So this is all for this blog folks, thanks for reading it and I hope you are taking something with you after reading this and till the next time ?…

Read my previous post: Movie Recommendation System in 2 ways

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