Objective: Employing automated language analysis, specifically Meaning Extraction Method (MEM) and Principal Component Analysis (PCA), to identify key factors in open-text responses about hearing aid experiences.

Design: An exploratory, cross-sectional design. Participants completed an online questionnaire encompassing demographic, audiological, and open-ended questions concerning hearing aid experiences, the International Outcome Inventory for Hearing Aids, general health, well-being, and social networks questions. Five hundred thirty-eight participants’ responses to a single open-ended question were analyzed using MEM, PCA, regression, and correlation analyses.

Results: The MEM-derived items revealed six factors related to hearing aid experiences: 1) life change, 2) social situation, 3) quality of life, 4) impact and speech understanding, 5) communication and interaction, and 6) music and environmental sounds. Most statistically significant correlations between the IOI-HA and the PCA factors were observed in IOI-HA item 3 (i.e., residual activity limitations). The quantile regression results revealed that PCA factors F1 and F2 added statistically significantly to the prediction of the IOI-HA total score. Positive correlations were observed between self-reported hearing difficulty and the first, fourth, and fifth factors as well as between factor one and general health, as well as factor two and physical activity.

Conclusion: Analyzing open-ended text responses through natural language analysis can offer valuable insights into the lived experiences of hearing aid users. Future studies should aim to refine this methodology for examining hearing aid experiences, enhancing clinical relevance and generalizability.