Implementation of Integrated Bayes Formula and Support Vector Machine for Analysing Airline’s Passengers Review

Abstract

Nowadays, the utilization of Internet of Things (IoT) is commonly used in the tourism industry, including aviation, where passengers of flight services can rate their satisfaction levels towards the product and service they use by writing their reviews in the form of text-based data on many popular websites. These passenger reviews are collections of potential big data and can be analyzed in order to extract meaningful informations. Some text mining algorithms are already in common use, including the Bayes formula and Support Vector Machine methods. This research proposes an implementation of the Bayes and SVM methods where these algorithms will operate independently yet integrated with other modules such as input data, text pre-processing and shows output result concisely in one single information system. The proposed system was successfully delivered 1000 documents of passenger reviews as input data, then after implemented the pre-processing method, the Bayes formula was used to classify the document reviews into 5 categories, including plane condition, flight comfort, staff service, food and entertainment, and price. While simultanously, the positive and negative sentiment contained in the review document was analyzed with SVM method and shows the accuracy score of 83.6% for a training to testing set ratio of 50:50, while 82.75% accuracy for the 60:40 ratio, and 83.3% accuracy for the 70:30 ratio. This research shows that two different text mining algorithms can be implemented simultaneously in a effective and efficient way, while still providing an accurate and satisfying performance results in one integrated information system.

Sentiment classification from reviews for tourism analytics

“…It can also classify a review's emotions and polarity classes for text classifications [2] [12] [32] [34] [35] [40]. The SVM classifier can solve binary classification problems [40] [41] with a representation of feature vectors by using the Bag-of-Words model [10]. RF can be used for regression and classification tasks and is known as an ensemble technique because it combines multiple models to make predictions.…”

“…In another study, Twitter data related to two top apparel international brands was compared and analyzed using Naive Bayes and lexicon dictionary to get the public opinions of the two brands [2]. In paper [10] , airline passenger reviews were classified into 5 categories: plane condition, flight comfort, staff service, food and entertainment and price using the Bayes and Support Vector Machine method to understand the satisfaction levels of the passenger for these categories. Moreover, people's perceptions regarding vaccines in Indonesia on Twitter were captured in the first two weeks to predict the people's sentiment about it by using Support Vector Machine and Random Forest [11].…”

Sentiment Analysis Based on Travelers’ Reviews Using the SVM Model with Enhanced Conjunction Rule-Based Approach

“…M.D. Devika et al, [8] Describe sentiment analysis as a process of interpreting user emotions, which falls under the domain of Natural Language Processing (NLP). The rise of internet-based applications has led to an increase in personalized reviews to assist travelers and customers in their decision-making process.…”

A BWCNN-GRU-LSTM-ATT model-based sentiment classification method for civil aviation passengers

“…For example, Yu Junzheng et al [1] proposed a hybrid algorithm-deep neural network (DNN) combining gradient-based approach and evolutionary algorithm to train PFDBM, which classifies normal passengers and potential attackers; Andrea Tundis et al [2] used a neural network approach to identify potential terrorists in the network using social network information. Aditya Tegar Satria et al [3] used Bayesian and support vector machine approaches for the classification of civil aviation passengers into five categories for airline related services in social media; Han Ping et al [4] proposed a microblogging text sentiment analysis model E-DiSAN based on sentiment fusion and multidimensional self-attention mechanism to classify the sentiment of civil aviation passengers using microblogging information; Yang Hongjing [5] uses SVM to classify the text of civil aviation comments obtained from microblogs. However, the above-mentioned research methods have a single network structure, which basically focus on the depth of the network and ignore the influence of the network width on feature extraction, and the selection of hyperparameters is often based on experience to manually adjust the parameters, which is time-consuming and does not guarantee the best accurate characterization.…”