Ensemble Sentiment Model: Bagging with Linear Discriminant Analysis (BLDA)

Rapid development in information technology and social media platforms increased the demand for sentiment analysis and modelling. Online reviews contain information about customer choice towards a particular product or a service. Existing approaches of sentiment analysis worked on a single learner or predefined ensemble learner to classify the sentiments. This work proposed a bagging ensemble approach with Linear Discriminant Analysis (BLDA) model for analysing the restaurant reviews. The Linear Discriminant Analysis (LDA) was chosen as a base classifier for each random subset in the bagging meta-estimator model. Our solution mainly focuses on enhancement, standardisation, preprocessing, and performance of sentiment analysis with topic-modelling and ensemble learning. Topic-Modelling is used to find the context of a customer regarding a service. The BLDA model's effectiveness has tested on restaurant reviews with various measurement units as Recall, Precision, F1-Score and ROC-AUC curve. The comparative study with Gaussian Naïve Bayes (GaussianNB) and K-Nearest Neighbor (KNN) present the proposed model's best performance.