Sentiment Analysis Using Deep Learning

Executive Summary

• Topic: Sentiment Analysis using Deep Learning
• Deep Learning Model: Long Short-Term Memory (LSTM)
• Accuracy Achieved: 86%
• Operations Performed:
  • Basic Exploratory Data Analysis (EDA)
  • Data Preprocessing
  • Model Creation
  • Parameter Tuning
  • Prediction

Introduction

Sentiment Analysis is a widely used application of data science, particularly for analyzing user sentiments on social media platforms. This project uses a Twitter dataset to classify sentiments as positive, negative, or neutral, leveraging deep learning models to achieve meaningful insights from user opinions. The dataset is available on Kaggle.

Project Structure

• Data Preprocessing: Cleaning and transforming the Twitter dataset for modeling.
• Modeling: Implementation of LSTM for sentiment classification.
• Evaluation: Model evaluation to determine accuracy and optimize parameters.

Installation

To run this project, install the following libraries:

pip install numpy pandas matplotlib seaborn tensorflow keras streamlit