Otel Değerlendirmeleri Üzerinde Hedef Tabanlı Fikir Madenciliği

Yıl 2021, , 28 - 34, 30.12.2021

Yunus Emre Demir Semih Durmaz Ahmet Elbir , İbrahim Onur Sığırcı , Banu Diri

https://doi.org/10.7240/jeps.896515

Öz

Users often use online reviews to assess the quality of hotels according to their various attributes. In this study, a sentiment analysis of online reviews has been conducted using 11 predetermined attributes pertaining to hotels. Using this analysis, users’ overall assessments of hotels have been determined and summarized from reviews left for a group of various hotels. To identify words with similar meanings to the 11 predetermined hotel attributes, the Word2Vec method has been employed. Additionally, the FastText method has been used to detect words containing spelling errors. The sentiment analysis of the comments has been made by using three different methods belonging to two different approaches. These methods are: VADER method as dictionary-based approach, BERT and RoBERTa as machine learning approaches. Using these methods, the reviews have been evaluated in three categories as positive, negative, and neutral, and the quality score has been calculated. In addition, a software with a user-friendly graphical interface has been implemented in an effort to easily use all the methods used in this study.

Anahtar Kelimeler

fikir madenciliği, duygu analizi, hedef tabanlı, sosyal medya, otel yorumları

Teşekkür

Bu çalışma ASYU2020_Akıllı Sistemlerde Yenilikler ve Uygulamaları Özel sayısı için değerlendirilmek üzere gönderilmiştir

Aspect Based Opinion Mining on Hotel Reviews

Öz

Users often use online reviews to assess the quality of hotels according to their various attributes. In this study, a sentiment
analysis of online reviews has been conducted using 11 predetermined attributes pertaining to hotels. Using this analysis, users’
overall assessments of hotels have been determined and summarized from reviews left for a group of various hotels. To identify
words with similar meanings to the 11 predetermined hotel attributes, the Word2Vec method has been employed. Additionally,
the FastText method has been used to detect words containing spelling errors. The sentiment analysis of the comments has
been made by using three different methods belonging to two different approaches. These methods are: VADER method as
dictionary-based approach, BERT and RoBERTa as machine learning approaches. Using these methods, the reviews have been
evaluated in three categories as positive, negative, and neutral, and the quality score has been calculated. In addition, a software
with a user-friendly graphical interface has been implemented in an effort to easily use all the methods used in this study.

Anahtar Kelimeler

opinion mining, sentiment analysis, aspect based, social media, hotel reviews

Kaynakça

• Sentiment analysis, https://monkeylearn.com/sentiment-analysis/, (2020)
• Eroğul, U. (2009). Sentiment analysis in Turkish (Master's thesis).
• Vural, A. G., Cambazoglu, B. B., Senkul, P., & Tokgoz, Z. O. (2013). A framework for sentiment analysis in turkish: Application to polarity detection of movie reviews in turkish. In Computer and Information Sciences III (pp. 437-445). Springer, London.
• Aytuğ, O. N. A. N. (2018). Sentiment analysis on Twitter based on ensemble of psychological and linguistic feature sets. Balkan Journal of Electrical and Computer Engineering, 6(2), 69-77.
• Nizam, H., & Akın, S. S. (2014). Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması. XIX. Türkiye'de İnternet Konferansı, 1-6.
• Symeonidis. S, https://www.kdnuggets.com/2018/03/5-things-sentiment-analysis-classification.html, (2018)
• Kharde, V., & Sonawane, P. (2016). Sentiment analysis of twitter data: a survey of techniques. arXiv preprint arXiv:1601.06971.
• Kan. D, Sentiment analysis, https://www.quora.com/What-is-the-difference-between-the-corpus-based-approach-and-the-dictionary-based-approach-in-sentiment-analysis, (2020)
• Ling, W., Dyer, C., Black, A. W., & Trancoso, I. (2015). Two/too simple adaptations of word2vec for syntax problems. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 1299-1304).
• What is fasttext? Are there tutorials? , https://fasttext.cc/docs/en/faqs.html, (2020)
• Fivez, P., Suster, S., & Daelemans, W. (2017, August). Unsupervised context-sensitive spelling correction of clinical free-text with word and character n-gram embeddings. In BioNLP 2017 (pp. 143-148).
• Pandey, P. (2018). Simplifying sentiment analysis using VADER in Python (on social media text). Retrieved from Analytics Vidhya website: https://medium. com/analytics-vidhya/simplifying-socialmedia-sentiment-analysis-using-vader-in-python-f9e6ec6fc52f.
• Horev, R. (2018). BERT Explained: State of the art language model for NLP. Towards Data Science, Nov, 10.
• Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.