Recent decades have witnessed significant advancements in text analysis, with notable developments in Latent Dirichlet Allocation (LDA) and Structural Topic Modeling (STM). These techniques effectively uncover underlying patterns and structures within textual data. Furthermore, neural networks, particularly Transformer-based models, have revolutionized text analysis by leveraging attention mechanisms to process input signals simultaneously, leading to more efficient and accurate results. These advancements have proven invaluable in analyzing consumer reviews of business products and public input on government policies. Neural networks, in particular, excel at sentiment analysis, providing valuable insights into public opinion and sentiment trends.

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