https://github.com/adithivs/financial-sentiment-analysis-using-lstm
https://github.com/adithivs/financial-sentiment-analysis-using-lstm
Last synced: over 1 year ago
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- Host: GitHub
- URL: https://github.com/adithivs/financial-sentiment-analysis-using-lstm
- Owner: AdithiVS
- License: bsd-2-clause
- Created: 2024-11-09T07:19:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-14T13:19:34.000Z (over 1 year ago)
- Last Synced: 2025-01-21T20:14:56.412Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 457 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Financial-Sentiment-Analysis-using-LSTM
## Overview
This project aims to perform sentiment analysis on financial texts, classifying them into three sentiment categories: positive, negative, and neutral. The model is built using a Long Short-Term Memory (LSTM) network, a deep learning model well-suited for sequential data like text. This project demonstrates how to preprocess text data, build and train an LSTM model, and evaluate its performance on a labeled financial dataset.
## Problem Statement
The goal of this project is to classify the sentiment of financial texts, such as news articles, reports, and social media posts. By identifying whether the sentiment expressed in the text is positive, negative, or neutral, this analysis can be used to support decision-making in financial markets, such as for stock market predictions, investor sentiment analysis, and risk management.
## Objectives
- Preprocess raw text data for sentiment analysis.
- Build a robust LSTM-based deep learning model for sentiment classification.
- Train the model on labeled financial text data.
- Evaluate model performance using accuracy, precision, recall, and F1-score.
- Visualize results through confusion matrices and performance metrics.