Text Annotation Tools: A Comprehensive Review and Comparative Analysis

This paper focuses on the comparative analysis of textual annotation tools present in the literature, developed to facilitate the creation of textual training sets in response to the widespread application of Machine Learning (ML) and Natural Language Processing (NLP). The increasing importance of storing all annotations generated by various domain experts within a knowledge base emerges as a crucial issue. This is due to the fact that the quality of results obtained from Data Mining and Text Mining models heavily relies on the number of documents that have been semantically annotated. Consequently, the article proposes a detailed comparison of the main textual annotation tools described in scientific literature. This analysis aims to provide a comprehensive review of the features, functionalities, and performances of these tools, facilitating the selection and implementation of the most suitable tools for the specific needs of various application contexts. In conclusion, the article aims to contribute to a critical understanding of textual annotation tools, offering users and developers an in-depth analysis to guide choices in the implementation of ML and NLP-based models. This, in turn, promotes the development of more efficient and precise solutions in the context of text analysis.