AUTOMATED TEXT ANALYSIS OF HISTORICAL DOCUMENTS USING MACHINE LEARNING TECHNIQUES - Lee Bih Ni (JOURNAL INFORMATION AND TECHNOLOGY MANAGEMENT (JISTM), 2024)
This paper presents an innovative approach to the automated analysis of historical documents through the application of advanced machine learning techniques. The problem background in automated text analysis of historical documents using machine learning techniques is efficiently processing large volumes of diverse historical texts to extract valuable insights and patterns, enhancing historical research and understanding. Leveraging the power of natural language processing, this study proposes a comprehensive framework that encompasses data preprocessing, feature extraction, and model training, enabling the efficient extraction of valuable insights from vast collections of historical texts. By utilizing cutting-edge algorithms, including deep learning and sentiment analysis, the research demonstrates the potential of this approach in uncovering hidden patterns, sentiments, and semantic nuances within historical documents, thereby facilitating a deeper understanding of the past and shedding light on critical historical events and societal developments.