Sentiment analysis can be considered a critical task in the field of Natural Language Processing (NLP). It is applied not only in product reviews but also in areas like healthcare, education, services assessment, and so on. While there have been remarkable advancements in building text-based sentiment models, the problem of the interaction between structured and unstructured data for sentiment analysis has not been addressed adequately. Specifically, current systems use scale ratings and descriptive texts separately without considering how one can complement the other. This chapter provides an in-depth discussion of the dual-channel sentiment model based on the Average Cumulative Rating Matrix Factorization (ACRMF) model presented in Kumar et al. [7]. In addition to utilizing internal review comments, namely structured numerical scale ratings, and external review comments, open-ended descriptive text, the approach creates a common sentiment score based on the normalized averaging method. The mathematical concepts underpinning the framework include Bayesian probability theory, the Naïve Bayes classifier, Part-of-Speech tagging, the Stanford Sentiment Treebank, and Recursive Neural Networks. Three research hypotheses are formulated, which are tested on two real datasets: Tourpedia and Kaggle Travel Review Rating. Experiments are conducted for two different train-test splits, three different cut-off values, and seven baseline methods. Under all experimental conditions, the dual-channel sentiment model outperforms its single-channel counterpart in a statistically significant way. This section is concluded with a limitations analysis and an outline for further research directions.