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News plays a major role in how people and markets react to stocks. A single article or press release can cause a sharp change in stock price.

Understanding the sentiment behind financial news can help investors make better decisions.

In this article, I will show you how to build a simple sentiment analysis pipeline for stock-related news using FinBERT, a language model designed specifically for financial text.

We’ll use Python, the Hugging Face transformers library, and yfinance to collect news headlines and summaries. Then we'll classify each news summary as:

• positive

• negative or

• neutral

Finally, we'll save and visualize the sentiment distribution.

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What is FinBERT?

FinBERT is a version of the BERT model that has been fine-tuned on financial news articles. It was created by Prosus AI using the original BERT base model from Google.

Unlike general-purpose sentiment models, FinBERT is trained on financial language, which helps it understand domain-specific words and phrasing.

For example, a sentence like “profits decreased less than expected” may be seen as positive in financial contexts, even though it contains the word “decreased”.

FinBERT is designed to handle this kind of nuance.

Installing the Required Libraries

First, we need to install the necessary libraries. These include:

• yfinance for retrieving stock news

• transformers and torch for working with FinBERT

• pandas for handling data

• matplotlib plotting results.

%pip install yfinance transformers torch pandas matplotlib

This cell ensures all dependencies are installed in your Python environment.

Importing Python Libraries

Once the libraries are installed, we import them into our workspace.

import pandas as pd
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
import matplotlib.pyplot as plt
import yfinance as yf
plt.style.use("dark_background")

We import pandas to handle dataframes, matplotlib.pyplot for plotting, and several modules from transformers to load FinBERT.

yfinance is used to pull news related to a stock ticker.

Loading the FinBERT Model

Now we load the pre-trained FinBERT model and tokenizer.

# Load FinBERT for sentiment analysis
model_name = "ProsusAI/finbert"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

We specify the model name ProsusAI/finbert, which is available on Hugging Face. We create a sentiment classification pipeline using this model and tokenizer.

The pipeline will handle tokenization and prediction for each input string.

Fetching News from Yahoo Finance

We now fetch the latest news articles for a stock of interest.

ticker = "META"  # Example ticker symbol
dat = yf.Ticker(ticker)
news = dat.news

We create a Ticker object for Meta Platforms Inc. (formerly Facebook) and use the .news attribute to access recent news headlines and summaries.

Preparing the News Data

We convert the news into a structured format using a dataframe.

# Extract 'content' part into a DataFrame
df = pd.DataFrame([item["content"] for item in news])

# Keep only desired columns
columns_to_keep = ['title', 'summary', 'pubDate']
df = df[columns_to_keep]

# Display result
df.head(100)

Each news item is stored as a dictionary. We extract the "content" part of each item and create a dataframe.

Then we keep only the columns we need:

• the title,

• summary,

• and publication date.

Performing Sentiment Analysis

Now we apply FinBERT to each news summary.

df["sentiment"] = df["summary"].apply(lambda x: classifier(x)[0]['label'])
df["confidence"] = df["summary"].apply(lambda x: classifier(x)[0]['score'])

# Optional: Clean up label casing
df["sentiment"] = df["sentiment"].str.lower()
df.head()

We use the sentiment classifier to predict the sentiment of each summary. The .apply function runs the classifier on each row and extracts both the label and confidence score.

We also convert the sentiment labels to lowercase for consistency.

Saving the Results

We can now save the results to a CSV file for future analysis.

# Save to CSV
filename = f"{ticker}_news_sentiment.csv"
df.to_csv(filename, index=False)
print(f"Saved sentiment results to {filename}")

This cell creates a filename based on the stock ticker and writes the dataframe to a CSV file. The index=False argument ensures that row indices are not saved.

Visualizing the Sentiment Distribution

Finally, we visualize how many news items were classified as positive, neutral, or negative.

# Visualize sentiment
sentiment_counts = df["sentiment"].value_counts()
sentiment_counts.plot(kind="bar", title=f"{ticker} News Sentiment", color=["blue", "green", "red"])
plt.ylabel("Count")
plt.xticks(rotation=0)  # Set x-axis labels to horizontal
plt.tight_layout()      # Adjust layout to prevent clipping
plt.savefig(f"{ticker}_sentiment_counts.png")
plt.show()

We count how many times each sentiment label appears and use a bar chart to show the distribution. Green represents positive news, red represents negative, and gray is for neutral sentiment.

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