Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework

The rise of online rental platforms has led to an overwhelming amount of user-generated content, making it difficult for prospective consumers to discern which reviews are helpful. Existing approaches often rely on raw helpfulness votes, which are sparse, subjective, and temporally inconsistent. Also, there is lack of labeled dataset in the field of rental review usefulness prediction. This paper introduces a novel dataset of apartment reviews collected from online website and proposes an intelligent machine learning framework to predict the helpfulness of rental reviews. To address the challenge of obtaining reliable labels from sparse and subjective user votes, a scoring-based labeling strategy is developed that uses helpful vote count and timeliness. A diverse set of features including TF-IDF vectors, sentiment polarity, rating deviation, and review length are used to capture both textual and behavioral aspects of the reviews. Multiple classifiers, including Logistic Regression, Naive Bayes, and XGBoost, are systematically evaluated under 5-fold cross-validation, along with a rule-based and deep learning models. Experimental results show that XGBoost consistently achieves the best overall performance with an accuracy of 0.71 and ROC-AUC of 0.75 when leveraging all features. This research makes three key contributions: (i) the first large-scale dataset for rental review, (ii) auto annotation technique that uses clustering approach with score from user votes and time since posted, and (iii) comprehensive evaluation pipeline spanning rule-based, traditional, and deep learning classifiers. Together, these advances establish a foundation for intelligent rental review helpfulness estimation, with broader implications for e-commerce, hospitality, and user-generated content analysis. • This paper Introduces a novel and comprehensive dataset of rental reviews from Apartments.com. • Proposes a scoring-based labeling strategy combining helpful vote count and review timeliness. • Achieves strong performance (ROC-AUC of 0.75 with XGBoost) in predicting review helpfulness. • Compares traditional classifiers (Logistic Regression, Naive Bayes, XGBoost) and deep learning models (LSTM, Transformer).