TextNa

TextNa is a modular, open-source library for text analysis and natural language processing. It provides a set of interoperable components for tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, language detection, and text normalization. The library emphasizes simplicity, readability, and performance, aiming to support both educational use and rapid prototyping as well as lightweight production tasks.

TextNa operates as a collection of pipeline components that can be composed to form end-to-end analyses. Each stage consumes and outputs structured annotations, enabling flexible experimentation and reuse. It supports batch and streaming processing, multiple language profiles, and a pluggable backend for rule-based or machine-learned components. The API is uniform across components to ease integration with other systems.

Implementation and usage: TextNa is primarily implemented in Python, with optional native extensions to improve performance. It can be installed from standard package repositories, and users can load text, run a pipeline of analyzers, and export the resulting annotations in JSON or tabular form for downstream tasks. The library includes predefined pipelines and supports user-defined configurations to reduce boilerplate.

History and reception: TextNa originated in the open-source NLP community as a lightweight tool intended for learning and quick experiments. It has seen adoption in tutorials, classroom demonstrations, and small-scale research projects. Documentation highlights extensibility, testing practices, and compatibility with external models and data formats.

See also: natural language processing, tokenization, named entity recognition, sentiment analysis.