Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

Abstract

Now-a-days, social media sites and travel blogs have become one of the most vital expression sources. Tourists express everything related to their experiences, reviews, and opinions about the place they visited. Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development. Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists. The tourism industry anywhere also relies heavily on the opinions of former tourists. However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system. Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects. one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development. Although a few lexicons are available to the public, they are sparse and small. Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques. Machine learning techniques help classifying these reviews into (positive, negative, and neutral). In this paper, three machine learning algorithms were used, Support -Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN). These algorithms are classified using Google Map data set for tourist places in Saudi Arabia. Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and Fscore. The results show that the SVM algorithm outperforms the deep learning techniques. The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.

Bridging the Kuwaiti Dialect Gap in Natural Language Processing

“…In [28], the authors introduced the first Emirati sentiment analysis dataset, which consists of 70,000 Instagram comments. The Saudi dialect is the most studied Gulf dialect, several sentiment analysis resources were created including but not limited to the followings: (1) [29] develop 2010 posts dataset for sentiment analysis; (2) [30] collect 32,063 Saudi posts; (3) [31] construct a dataset of 11,764 posts about Saudi universities; (4) in [32], the authors create a dataset of 22,433 reviews of tourist places.…”