The rapid evolution of quantum hardware is propelling quantum computing to new frontiers. Nonetheless, the potential of natural language processing in the quantum paradigm (QNLP) is yet to be explored, including for Noisy Intermediate-Scale Quantum (NISQ) machines. To explore the QNLP frontier, we introduce LEXIQL, a novel noise-aware QNLP technique for text classification on NISQ quantum machines. LEXIQL employs an incremental data injection approach to process textual data in a quantum circuit. It also develops new and effective training methods, such as leveraging a diverse mix of expressible and shallow quantum circuits for the QNLP task of text classification. Our extensive evaluation using Yelp, IMDB, and Amazon datasets (along with synthetic QLNP datasets) demonstrates the effectiveness of LEXIQL’s noise-aware design in both ideal and noisy environments.