https://github.com/cordeiroandres/sentiment-analysis-for-predicting-stock-market-movements-using-news-headlines

Sentiment Analysis for Predicting Stock Market Movements using News Headlines
https://github.com/cordeiroandres/sentiment-analysis-for-predicting-stock-market-movements-using-news-headlines

headlines jupyter-notebook machine-learning-algorithms nlp python3 stock-price-prediction text-analysis

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Sentiment Analysis for Predicting Stock Market Movements using News Headlines

• Host: GitHub
• URL: https://github.com/cordeiroandres/sentiment-analysis-for-predicting-stock-market-movements-using-news-headlines
• Owner: cordeiroandres
• Created: 2023-08-09T15:52:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-11T06:46:27.000Z (over 1 year ago)
• Last Synced: 2023-09-11T07:44:30.741Z (over 1 year ago)
• Topics: headlines, jupyter-notebook, machine-learning-algorithms, nlp, python3, stock-price-prediction, text-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 38.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Sentiment-Analysis-for-Predicting-Stock-Market-Movements-using-News-Headlines

Sentiment Analysis for Predicting Stock Market Movements using News Headlines

![download](https://techcrunch.com/wp-content/uploads/2019/09/GettyImages-1058454392-e1643158652665.jpg?w=1390&crop=1)

### Description

Sentiment analysis can be used to analyze news headlines in order to forecast the movement of the

stock market. The rationale behind this method is that news headlines can contain information

that might affect the mood of investors, and therefore influence stock prices.

Trying to predict the stock market movements is always a hard task, most of the people fail

at this challenge, many studies have shown that news sentiment can be a valuable predictor and

this has led to to the development of various sentiment analysis techniques and tools for financial

analysts and investors.

By analyzing the sentiment of news headlines related to specific companies or sectors, investors

can gain insights into the potential movements of stock prices and make informed investment

decisions.

The main objective of this project is to explore the use of sentiment analysis techniques

to predict stock market movements by analyzing news headlines. The goal is to identify the

sentiment of the headlines of the day and extract meaningful insights that can inform investment

decisions. The aim is to develop a reliable and accurate predictive model that can be used to

support investment strategies and maximize returns in the stock market.

The project was entirely developed in Python and Jupyter notebooks. We have analyzed

the dataset to understand the distribution of the labels and how they are related to each other.

Noise has been removed from each headline also the stopwords, reducing the remaining terms

to the basic form (stemming and lemmatization). We have apply varius classifiers, such as

random forest,XGBoost,and more advanced models like Convolutional Neural Network, Long

Short-Term Memory(LSTM) and pre-trained models like BERT and RoBERTa. For more details

and information about the dataset, please refer to Kaggle’s page (https://www.kaggle.com/datasets/aaron7sun/stocknews)