Abstract:
Sentiment analysis plays a crucial role in understanding public opinion and sentiment towards products, services, and events. This project aims to develop a natural language processing (NLP) model for sentiment analysis in social media. By leveraging machine learning algorithms and NLP techniques, the proposed model will analyze text data from social media platforms, classify the sentiment of the text as positive, negative, or neutral, and provide insights into public sentiment trends. The model’s accuracy and performance will be evaluated using a dataset of social media posts, and its potential for real-time sentiment analysis applications will be explored.

Table of Contents:
Chapter 1: Introduction
1.1 Background and Motivation
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations
1.5 Methodology

Chapter 2: Literature Review
2.1 Overview of Sentiment Analysis in Social Media
2.2 Natural Language Processing Techniques for Sentiment Analysis
2.3 Machine Learning Algorithms for Sentiment Classification
2.4 Feature Extraction and Selection Methods
2.5 Summary of Existing Research

Chapter 3: Data Collection and Preprocessing
3.1 Social Media Data Acquisition
3.2 Data Cleaning and Transformation
3.3 Text Preprocessing and Tokenization
3.4 Data Splitting and Cross-Validation

Chapter 4: Sentiment Analysis Model Development
4.1 Feature Extraction and Representation Techniques
4.2 Sentiment Classification Algorithms
4.3 Model Training and Optimization
4.4 Performance Evaluation and Validation

Chapter 5: System Implementation and Evaluation
5.1 System Architecture and Integration
5.2 Real-Time Sentiment Analysis on Social Media Data
5.3 Performance Evaluation and Comparison
5.4 User Interface Design and Visualization of Sentiment Trends
5.5 Ethical Considerations and Bias Analysis

5.5 Conclusion and Future Work