Twitter Sentiment Analysis on Stocks
This project analyzes Twitter sentiment related to stock market discussions to understand how social media opinions correlate with stock movements.
The goal of the project was to extract insights from large volumes of Twitter data and classify tweets based on their sentiment toward specific stocks. By applying Natural Language Processing (NLP) and machine learning techniques, the model identifies whether tweets express positive, negative, or neutral sentiment.
The workflow includes data preprocessing, feature extraction from text, sentiment classification, and analysis of sentiment trends related to financial markets. This project demonstrates how social media data can be leveraged for financial analytics and market sentiment understanding.
You can explore the project repository here:
