Natural language processing for sentiment analysis
Table Of Contents
1. Introduction
1.1 Background of sentiment analysis
1.2 Applications of sentiment analysis
1.3 Objectives of the project
2. Text Preprocessing and Feature Extraction
2.1 Tokenization and normalization
2.2 Feature representation techniques
3. Sentiment Analysis Techniques
3.1 Lexicon-based methods
3.2 Machine learning models for sentiment classification
3.3 Deep learning approaches
4. Domain-specific Sentiment Analysis
4.1 Challenges in domain adaptation
4.2 Transfer learning for sentiment analysis
5. Evaluation Metrics and Performance Analysis
5.1 Accuracy, precision, and recall
5.2 Cross-domain evaluation
Thesis Abstract
This project aims to explore natural language processing techniques for sentiment analysis of textual data from social media, customer reviews, and other sources. The project will involve text preprocessing, feature extraction, and the application of machine learning models to classify the sentiment of the text as positive, negative, or neutral. The project will also investigate the challenges of sentiment analysis in different domains and languages.
Thesis Overview
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