Exploring the Effects of Personality Traits on Customer Perceived Value Based on Text Decomposition

Customer perceived value (CPV) is pivotal for hospitality competitiveness, but the impact of personality traits on CPV remains underexplored. This study examines how personality traits influence CPV in the hotel industry using advanced text mining and NLP techniques. By analyzing TripAdvisor reviews, we propose a framework that integrates LDA topic modeling, multi-level sentiment classification with an improved Doc2Vec-IOVO strategy, and a CNN-LIWC based personality trait recognition model to extract perceptual factors and infer reviewers’ Big Five personality traits. Our regression analysis reveals significant relationships between personality traits and CPV dimensions. Finally, we explore the predictive value of the personality measures. Incorporating new features into the model increases the recognition accuracy by up to 2.66%. Among the Big Five personality traits, extraversion, conscientiousness, and openness positively affect perceived value, whereas neuroticism has a negative effect. Extraversion and neuroticism exert a stronger influence than the other traits. The results demonstrate the significant predictive value of personality indicators. Compared with traditional surveys, NLP-based personality identification from online reviews offers a more efficient approach, enabling personalized recommendations to enhance customer satisfaction in the hotel industry.