Twitter Sentiment Analysis

Overview

Developed a machine learning model to analyze sentiment in Twitter data, classifying tweets as positive, negative, or neutral with 87% accuracy.

Methodology

Used a combination of NLP techniques and deep learning models. Preprocessed text data using tokenization, stemming, and removing stop words. Implemented a LSTM neural network for classification.

Key Findings

• LSTM models outperformed traditional machine learning approaches by 12%
• Preprocessing steps significantly improved model performance
• Emoticons and hashtags were strong indicators of sentiment

Data Visualization