Implicit Aspect Analysis for Sentiment Classification: A Case Study on Hotel Reviews

Abstract

Aspect-Based Sentiment Analysis (ABSA) plays a vital role in interpreting user opinions in customer reviews by identifying sentiment polarity linked to specific service aspects. However, traditional ABSA methods largely focus on explicit aspect mentions, often neglecting implicit aspects that are contextually implied. This limitation is especially critical in the hospitality domain, where customer reviews frequently include indirect references to hotel features. This study proposes an implicit aspect analysis framework for sentiment classification using hotel reviews, aiming to improve the detection and interpretation of implicit aspect expressions. A dataset of 2,000 TripAdvisor hotel reviews was collected and manually annotated by linguistic experts for four aspects: staff service, cleanliness, value for money, and location convenience. Each sentence was labeled as either explicitly or implicitly expressing an aspect, with associated sentiment polarity (positive or negative). A two-stage framework was developed: the first stage employed Word2Vec Skip-gram modeling and TF-IDF to construct an implicit aspect corpus; the second stage implemented machine learning and deep learning classifiers—including SVM, LR, MNB, RF, CNN, and LSTM—to predict sentiment. The hybrid feature representation combining TF-IDF and Word2Vec significantly enhanced the model's ability to detect implicit sentiments. Deep learning models, particularly LSTM and CNN, outperformed traditional classifiers in both sentiment polarity classification and implicit aspect association. Clustering results confirmed strong alignment with expert-labeled data, validating the framework’s effectiveness. The proposed framework demonstrates substantial improvements in classifying implicit aspects, offering valuable insights for service quality evaluation in hospitality and other domains relying on user-generated content.