Examinando por Autor "Springer"

Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Activity-Based Anorexia
    (Springer, 2020) Paz Regidor, Ana María de; Vidal García, Pedro; Pellón Suárez de Puga, Ricardo; Springer
  • Miniatura
    Publicación
    Analyzing customer satisfaction through online reviews using topic modeling and linguistic model: A study of a hotel chain in Madrid
    (Springer, 2025-06-01) Shu, Ziwei; Sánchez Figueroa, María Cristina; Springer
    Abstract With the widespread use of social media and review platforms, Electronic Word of Mouth (eWOM) has a greater reach and influence than traditional Word of Mouth (WOM), becoming a vital information source for customers and companies. Online reviews, a representative type of eWOM, are essential for guiding customer hotel choices and helping hotel managers gain valuable insights for necessary improvements. Mining online reviews and sentiment analysis are popular research areas, where sentiment analysis typically classifies the overall sentiment of customer reviews as positive, neutral, or negative. This study presents an approach for analyzing hotel customer satisfaction using the Latent Dirichlet Allocation (LDA) model and the 2-tuple linguistic model. The LDA model, widely used for topic modeling, is employed to identify the topics that matter more to customers and influence their satisfaction. The 2-tuple linguistic model is applied in sentiment analysis to address information loss when converting compound scores into sentiment labels, offering a clearer and more intuitive representation of sentiment while retaining the information. The proposed model analyzes 26,740 TripAdvisor reviews from twenty hotels in Madrid that are part of the same hotel chain. The results highlight the importance of considering variations in hotel scores and rankings across different languages, as identical rankings do not always correspond to the same sentiment scores from English-speaking and Spanish-speaking reviews, and vice versa. This study contributes to online reviews research and hotel management by offering a clearer and more accurate expression of customer review sentiment through 2-tuple values, and the most frequent topics from reviews.