Understanding Natural Language Processing (NLP) Techniques

Abstract

Natural Language Processing stands at the intersection of Data Science, linguistics, computer science, and artificial intelligence, offering powerful methodologies to analyze and generate human language. The theoretical foundations and practical applications of NLP techniques are revealed with a specific focus on sentiment analysis and language generation. The evolution of NLP from rule-based systems to sophisticated neural architectures is presented, highlighting how these advancements have transformed machines' ability to comprehend nuanced emotional content and produce coherent text. Preprocessing techniques, traditional and contemporary methods for sentiment classification, and the revolutionary impact of transformer-based models on language generation capabilities are encompassed. These complementary domains demonstrate how sentiment analysis extracts meaning from existing text while generation systems create new linguistic content, together forming the backbone of many modern language technologies that increasingly mediate human-computer interaction in everyday applications.