Aspect-based sentiment score and star rating prediction for travel destination using Multinomial Logistic Regression with fuzzy domain ontology algorithm

Abstract

Due to its significant advantages for downstream applications, such as recommender systems, In the context of travel and tourism, user reviews on TripAdvisor have an impact on other travellers' judgments regarding a variety of travel-related issues, including the choice of a vacation spot, lodging, and places to visit. In graded user reviews, the model must specifically forecast the user's review score after receiving the textual review. The purpose of this article is to present a predictive outline for aspect-based extraction and classification that can estimate the users' optimal travel destination. This impression helps travellers in a variety of ways, such as by recommending better locations that also include expensive destinations. The underlying classification algorithm's processing time is significantly impacted by more dimensions. The term “curse of dimensionality” is often used in statistics and machine learning to describe these issues. By projecting the high-dimensional input data into the low-dimensional subspace while roughly maintaining the distance between the data points with a higher probability, the Random Projection (RP) ensemble classifier decreases the complexity of multivariate data. Extract the crucial information from the reviews, then use Glove word vector representation to categorise the relevant emotions. Further, the article proposed Multinomial Logistic Regression (MNLR) with a Fuzzy Domain Ontology (FDO) algorithm for aspect-based sentiment analysis. More intricate aspects than just the products themselves impact how satisfied people are with tourist destinations. The most important factor in evaluating how convenient a tourist route will be is typically the weather. The combined form of predicted sentiment score, start ratings and environment factor has to be calculated to predict the travel destination based on the measurement of personalized search results. The simulation was processed in Python software. The presented work has utilized some performance measures to evaluate the classification model such as F1-score, Recall, Precision, Mean Absolute Error (MAE), Mean Squared Error (MSE), Cohen Score and Matthew Score. The accuracy with GloVe word vector representation was 90% and after the GloVe representation, the classification model accuracy was about 94%. The proposed strategy outperforms in terms of classification accuracy, according to simulated results and analyses of real-world data.