SAHRAN: Sentiment Analysis of Hotel Reviews with Attention-Based Recurrent Neural Network

Authors : Halit Çetiner, Sedat Metlek

Pages : 39-56

Doi:10.21597/jist.1523220

View : 52 | Download : 36

Publication Date : 2025-03-01

Article Type : Research Paper

Abstract :Automatically analysing the sentiment of comments expressed by a user on a web page for any purpose is a rapidly expanding important research area. Text sentiment analysis, as it is known in the literature, is a technique that allows users to determine their emotional tendencies in comments defined for any purpose. Users comment on the content of web pages used by thousands of people such as vacation sites, shopping pages, social media, brand reviews, financial reviews, health sites, political pages. The comments made have the ability to directly affect a user who wants to benefit from these services in any way. For these reasons, it is important to examine people\\\'s emotions in their comments in automatic review of comments. Recurrent Neural Network (RNN) based architectures have achieved remarkable success in solving Natural Language Processing (NLP) problems. In this article, an RNN based deep learning model is proposed that works on a publicly available dataset obtained from the TripAdvisor web page and performs sentiment analysis. The proposed SAHRAN model uses an attention mechanism based on the dot product structure to capture emotional words in user comments. In the model, Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short Term Memory (BiLSTM) deep learning layers are integrated into the model to capture emotional features. As a result of the experimental studies, the proposed SAHRAN model achieved performance values of 0.9524, 0.9685, 0.9082 and 0.9338 in terms of precision, recall, F1 score and accuracy performance measures, respectively.
Keywords : RNN, Derin öğrenme, BiGRU, BiLSTM, Doğal dil işleme

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