Advancements and Methodologies in Natural Language Processing and Machine Learning: A Comprehensive Review

Abstract: This review explores notable advancements and methodologies in Natural Language Processing (NLP) and machine learning. Key innovations include convolutional neural networks capable of generating multiple language processing predictions and the Transformer-XL model, which enhances temporal coherence and dependency handling. The importance of logical reasoning and common sense in AI is emphasized, along with practical methods like web mining for building extensive knowledge repositories. The development of NLP tools for specific languages, such as Gujarati, and the utilization of BERT for bidirectional training are discussed, showcasing significant performance improvements across multiple tasks. Sentiment analysis is highlighted as a crucial tool for interpreting unstructured data from social media, achieving high accuracy through rule-based approaches and feature extraction methods. The role of social media in disseminating emotionally charged content and its impact on user behavior is examined, providing valuable insights for businesses to enhance marketing strategies. Additionally, the review addresses the challenge of early risk detection on social media platforms, emphasizing the need for effective preprocessing techniques to handle diverse and unstructured content. The integration of deep learning techniques in sentiment analysis is shown to offer competitive advantages, revealing consumer preferences and product trends.