Natural Language Processing in Artificial Intelligence: Techniques, Sentiment Analysis and Practical Applications

Abstract

With the rapid advancement of artificial intelligence, Natural Language Processing (NLP) has become a pivotal bridge between human communication and machine interpretation. This paper provides a comprehensive investigation into the conceptual framework, technical methodologies, and practical applications of NLP, with a focused examination of text sentiment analysis as a core NLP task. I detail methodological approaches ranging from lexicon-based to deep learning techniques, and demonstrate the implementation of sentiment analysis through a concrete case study using the Baidu artificial intelligence (AI) Open Platform. Experimental results from the case study illustrate the model’s ability to accurately capture overall sentiment, even in texts containing mixed expressions, for instance, correctly identifying a predominantly positive tone in a customer review despite minor criticisms. Furthermore, the study elucidates the substantial value of sentiment analysis across domains such as e-commerce, social media monitoring, and market research, while also addressing persistent challenges including context-dependency, figurative language, and multilingual processing. Finally, I outline emerging research directions aimed at enhancing the robustness, explainability, and applicability of sentiment analysis in real-world scenarios.