SAHRAN: Dikkat Tabanlı Yinelemeli Sinir Ağı ile Hotel Yorumlarının Duygu Analizi

Yıl 2025, Cilt: 15 Sayı: 1, 39 - 56, 01.03.2025

Halit Çetiner , Sedat Metlek

https://doi.org/10.21597/jist.1523220

Öz

Bir kullanıcının herhangi bir amaç içerisinde bulunan web sayfasında ifade edeceği yorumları otomatik olarak duygu yönünden analiz etmek hızla genişleyen önemli bir araştırma alanıdır. Literatürdeki adıyla metin duygu analizi, herhangi bir amaç ile tanımlanan yorumlardaki kullanıcıların duygusal eğilimlerini belirleyebilmeyi sağlayan bir tekniktir. Tatil siteleri, alışveriş sayfaları, sosyal medya, marka yorumları, finans yorumları, sağlık siteleri, siyaset sayfaları gibi binlerce insanın faydalandığı web sayfalarındaki içeriklerin kullanıcılar tarafından yorumlanması gerçekleştirilmektedir. Yapılan yorumlar, herhangi bir şekilde bu hizmetlerden faydalanmak isteyen bir kullanıcıyı doğrudan etkileme özelliğine sahiptir. Bu sebeplerden dolayı yorumların otomatik incelenmesinde insanların yorumlarındaki duygularını incelemek önem arz etmektedir. Yinelemeli Sinir Ağı (RNN) tabanlı mimariler Doğal Dil İşleme (NLP) problemlerinin çözümünde dikkat çekici başarılar sağlamıştır. Bu makale kapsamında tripadvisor web sayfasından elde edilen halka açık bir veriseti üzerinde çalışıp duygu analizi gerçekleştiren RNN tabanlı bir derin öğrenme modeli önerilmiştir. Önerilen SAHRAN modeli, kullanıcı yorumlarındaki duygusal sözcükleri yakalayabilmek için nokta çarpım yapısını temel alan bir dikkat mekanizması kullanılmıştır. Modelde, duygu özelliklerini yakalayabilmek için de Çift Yönlü Kapılı Yinelemeli Hücreler (BiGRU) ve Çift Yönlü Uzun Kısa Süreli Bellek (BiLSTM) derin öğrenme katmaları modele entegre edilmiştir. Yapılan deneysel çalışmalar neticesinde önerilen SAHRAN modeli hassasiyet, geri çağırma, F1 puanı ve doğruluk performans ölçütleri açısından sırasıyla 0.9524, 0.9685, 0.9082 ve 0.9338 performans değerlerini elde etmiştir.

Anahtar Kelimeler

RNN, Derin öğrenme, BiGRU, BiLSTM, Doğal dil işleme

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

Öz

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.

Anahtar Kelimeler

RNN, Deep learning, BiGRU, BiLSTM, Natural language process

Kaynakça

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