AI-Driven Sentiment Analysis: A Natural Language Processing Approach

Abstract

The exponential growth of user-generated textual data across digital platforms has intensified the need for automated systems capable of understanding human opinions and emotions. Sentiment analysis, a fundamental task of Natural Language Processing (NLP), plays a crucial role in extracting subjective information from unstructured text. Recent advancements in Artificial Intelligence (AI), particularly in machine learning and deep learning, have significantly enhanced the accuracy and scalability of sentiment classification models. This study presents an AI-driven sentiment analysis framework that integrates NLP preprocessing techniques with both traditional machine learning and deep learning approaches. A synthetic dataset simulating real-world textual feedback is employed to ensure ethical compliance and experimental reproducibility. Text data is preprocessed using tokenization, stop-word removal, and lemmatization, followed by feature extraction through TF-IDF vectorization and word embeddings. Sentiment classification is performed using Logistic Regression, Support Vector Machine, and Long Short-Term Memory (LSTM) models. The performance of these models is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that deep learning-based LSTM models outperform traditional classifiers by effectively capturing contextual and sequential dependencies in text. The findings validate the effectiveness of AI-driven NLP techniques for sentiment analysis and highlight their potential applicability in domains such as customer feedback analysis, social media monitoring, and decision-support systems.