Development of a Natural Language Processing System for Sentiment Analysis
Table Of Contents
<p>1. Introduction<br> 1.1 Background and Motivation<br> 1.2 Objectives and Contributions<br>2. Literature Review<br> 2.1 Overview of Sentiment Analysis Techniques<br> 2.2 Analysis of Existing NLP Systems for Sentiment Analysis<br>3. Data Collection and Preprocessing<br> 3.1 Acquisition of Labeled Sentiment Datasets<br> 3.2 Text Preprocessing and Feature Extraction<br>4. Sentiment Analysis Models<br> 4.1 Machine Learning and Statistical Approaches<br> 4.2 Lexicon-Based and Rule-Based Methods<br> 4.3 Deep Learning Architectures for Sentiment Classification<br>5. System Implementation<br> 5.1 Selection of NLP Libraries and Frameworks<br> 5.2 Development of Sentiment Analysis Modules<br></p>
Thesis Abstract
<p> This project focuses on the development of a natural language processing (NLP) system that performs sentiment analysis on textual data to extract and classify subjective information such as opinions, emotions, and attitudes. The system will utilize machine learning models, lexicon-based approaches, and deep learning techniques to accurately identify sentiment polarity and intensity in diverse text sources such as social media posts, product reviews, and news articles. The project aims to address the challenges of sentiment ambiguity, context sensitivity, and domain adaptation in sentiment analysis. <br></p>Thesis Overview
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