Sentiment analysis, also referred as opinion mining, is a valuable method for extracting sentiments from textual data. While traditional sentiment analysis focuses on quantifying the polarity of sentiment, this study proposes a novel method for extracting latent features and values from restaurant reviews. The proposed method makes use of a corpus of words containing meticulously annotated data about restaurants and employs a two-way approach that is tailored to effectively manage implicit aspects. Conditional Random Fields (CRF) with stochastic gradient descent optimization are used to further refine pretrained word embeddings, resulting in more accurate word representations and smoother sequential processing. We classify the recommended characteristics with remarkable precision using machine learning and ensemble techniques. The technique's utility and originality are exemplified by the fact that it resolves the double-implicit problem found in aspect and opinion mining. Extensive testing on a “hold-out” test set demonstrates that the enhanced CRF provides results that are significantly superior to those of reference systems, with a ROC-AUC of 96% and an F1 score of 94% for first-level entity extraction. In addition, ROC-AUC scores spanning from 71% to an astounding 94.8% are obtained for each improbable object by employing multiple machine learning and ensemble classifiers. This innovative two-tiered technique demonstrates its superiority and promise in the domain of implicit aspect extraction and categorization within the dynamic hospitality industry when compared to several works. The study's innovative methods and striking findings pave the way for enhanced opinion mining methodology and contribute significantly to our understanding of hotel guests’ opinions.