Araştırma Makalesi
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Sentiment Analysis of Restaurant Reviews in Artvin Province by Rule-based Sentiment Analysis and Machine Learning

Yıl 2022, Cilt: 5 Sayı: 2, 134 - 144, 29.07.2022
https://doi.org/10.53353/atrss.1090401

Öz

The purpose of this study was to investigate customer sentiments of restaurants in Artvin province. It was determined that 73.9% of the reviews were positive, and 26.1% were negative. 7 topics including place, view, price, food, service, staff and taste were extracted from the reviews. While the most reviews were about the place with 33.89%, it was followed by view with 15%, and the fewest reviews were about taste with 5.83%. It was found that the view topic was the most liked among these topics. 23.53% of those who commented on the price stated that the prices were high, while the percentage of those who indicated that the service was slow was 21.98%. In general, it was noticed that the service, place, food, and view topics were closely related to each other, and a customer who likes one of them is likely to appreciate the others and vice versa. It can be concluded that the application of RBSA and ML methods together is appropriate in terms of enabling both grammar rules and artificial intelligence methods and obtaining satisfactory results.

Kaynakça

  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
  • Akyol, C., & Zengin, B. (2021). Artvin Destinasyonundaki Turizm Faaliyetlerinin Geliştirilmesine Yönelik Kamu Paydaş Analizi. Journal of Tourism and Gastronomy Studies, 9(3), 1698-1721.
  • Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 1-23.
  • Bagheri, A., Saraee, M., & De Jong, F. (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52, 201-213.
  • Belkina, A. C., Ciccolella, C. O., Anno, R., Halpert, R., Spidlen, J., & Snyder-Cappione, J. E. (2019). Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature Communications, 10(1), 1-12.
  • Büyükeke, A., Sökmen, A., & Gencer, C. (2020). Metin madenciliği ve duygu analizi yöntemleri ile sosyal medya verilerinden rekabetçi avantaj elde etme: Turizm sektöründe bir araştırma. Journal of Tourism and Gastronomy Studies, 8(1), 322-335.
  • Dosoula, N., Griep, R., Ridder, R. d., Slangen, R., Luijk, R. v., Schouten, K., & Frasincar, F. (2016). Sentiment Analysis of Multiple Implicit Features per Sentence in Consumer Review Data. Paper presented at the DB&IS.
  • Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic markets, 25(3), 179-188.
  • Hasan, T., Matin, A., & Joy, M. S. R. (2020). Machine Learning Based Automatic Classification of Customer Sentiment. Paper presented at the 2020 23rd International Conference on Computer and Information Technology (ICCIT).
  • Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
  • Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of travel & tourism marketing, 30(1-2), 3-22.
  • Mahmood, A., & Khan, H. U. (2019). Identification of critical factors for assessing the quality of restaurants using data mining approaches. The Electronic Library.
  • Mathayomchan, B., & Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641.
  • Nakayama, M., & Wan, Y. (2019). The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews. Information & Management, 56(2), 271-279.
  • Oğan, Y., & Durlu Özkaya, F. (2018). Üniversite çalışanlarının yiyecek ve içecek işletmesi tercihleri üzerine bir araştırma. Uluslararası Artvin Sempozyumu, 18-20.
  • Oğan, Y., & Durlu Özkaya, F. (2021). Artvin'i Ziyaret Eden Turistlerin Gastronomi Deneyimleri Üzerine Bir İnceleme. Güncel Turizm Araştırmaları Dergisi, 5(2), 211-227.
  • Pantelidis, I. S. (2010). Electronic meal experience: A content analysis of online restaurant comments. Cornell Hospitality Quarterly, 51(4), 483-491.
  • Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. Paper presented at the In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks.
  • Romero, J. R., Roncallo, P. F., Akkiraju, P. C., Ponzoni, I., Echenique, V. C., & Carballido, J. A. (2013). Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Computers and Electronics in Agriculture, 96, 173-179.
  • Schmid, H. (1994). Part-of-speech tagging with neural networks. Paper presented at the In Proceedings of the International Conference on Computational Linguistics, pages 172-176.
  • Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060.
  • Zahoor, K., Bawany, N. Z., & Hamid, S. (2020). Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning. Paper presented at the 2020 21st International Arab Conference on Information Technology (ACIT).

Sentiment Analysis of Restaurant Reviews in Artvin Province by Rule-based Sentiment Analysis and Machine Learning

Yıl 2022, Cilt: 5 Sayı: 2, 134 - 144, 29.07.2022
https://doi.org/10.53353/atrss.1090401

Öz

Bu çalışmanın amacı Artvin ilindeki restoranların müşteri duygularını araştırmaktır. Müşteri değerlendirmelerin %73.9'unun olumlu, %26.1'inin olumsuz olduğu belirlenmiştir. Yorumlardan; yer, manzara, fiyat, yemek, servis, personel ve lezzet olmak üzere 7 farklı başlık elde edilmiştir. En fazla yorum %33.89 ile yer hakkında yapılırken, bunu %15 ile manzara takip etmiş ve en az yorum ise %5.83 ile lezzet hakkında yapılmıştır. Bu başlıklar arasında en beğenilen başlığın manzara olduğu tespit edilmiştir. Fiyat hakkında değerlendirme yapan müşterilerin %23.53'ü fiyatların yüksek olduğunu belirtirken, servisin yavaş olduğunu belirtenlerin oranı ise %21.98 olmuştur. Genel olarak servis, yer, yemek ve manzara başlıklarının birbiriyle yakından ilişkili olduğu ve bunlardan birini beğenen ya da beğenmeyen müşterinin diğerlerini de beğenme/beğenmeme durumunun yüksek olduğu görülmüştür. Hem dil bilgisi kurallarının hem de yapay zeka yöntemlerinin kullanılması ve doğru sonuçların elde edilmesi açısından RBSA ve ML yöntemlerinin birlikte kullanılmasının uygun olduğu sonucuna varılabilir.

Kaynakça

  • Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.
  • Akyol, C., & Zengin, B. (2021). Artvin Destinasyonundaki Turizm Faaliyetlerinin Geliştirilmesine Yönelik Kamu Paydaş Analizi. Journal of Tourism and Gastronomy Studies, 9(3), 1698-1721.
  • Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 1-23.
  • Bagheri, A., Saraee, M., & De Jong, F. (2013). Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems, 52, 201-213.
  • Belkina, A. C., Ciccolella, C. O., Anno, R., Halpert, R., Spidlen, J., & Snyder-Cappione, J. E. (2019). Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature Communications, 10(1), 1-12.
  • Büyükeke, A., Sökmen, A., & Gencer, C. (2020). Metin madenciliği ve duygu analizi yöntemleri ile sosyal medya verilerinden rekabetçi avantaj elde etme: Turizm sektöründe bir araştırma. Journal of Tourism and Gastronomy Studies, 8(1), 322-335.
  • Dosoula, N., Griep, R., Ridder, R. d., Slangen, R., Luijk, R. v., Schouten, K., & Frasincar, F. (2016). Sentiment Analysis of Multiple Implicit Features per Sentence in Consumer Review Data. Paper presented at the DB&IS.
  • Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic markets, 25(3), 179-188.
  • Hasan, T., Matin, A., & Joy, M. S. R. (2020). Machine Learning Based Automatic Classification of Customer Sentiment. Paper presented at the 2020 23rd International Conference on Computer and Information Technology (ICCIT).
  • Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
  • Leung, D., Law, R., Van Hoof, H., & Buhalis, D. (2013). Social media in tourism and hospitality: A literature review. Journal of travel & tourism marketing, 30(1-2), 3-22.
  • Mahmood, A., & Khan, H. U. (2019). Identification of critical factors for assessing the quality of restaurants using data mining approaches. The Electronic Library.
  • Mathayomchan, B., & Taecharungroj, V. (2020). “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management, 90, 102641.
  • Nakayama, M., & Wan, Y. (2019). The cultural impact on social commerce: A sentiment analysis on Yelp ethnic restaurant reviews. Information & Management, 56(2), 271-279.
  • Oğan, Y., & Durlu Özkaya, F. (2018). Üniversite çalışanlarının yiyecek ve içecek işletmesi tercihleri üzerine bir araştırma. Uluslararası Artvin Sempozyumu, 18-20.
  • Oğan, Y., & Durlu Özkaya, F. (2021). Artvin'i Ziyaret Eden Turistlerin Gastronomi Deneyimleri Üzerine Bir İnceleme. Güncel Turizm Araştırmaları Dergisi, 5(2), 211-227.
  • Pantelidis, I. S. (2010). Electronic meal experience: A content analysis of online restaurant comments. Cornell Hospitality Quarterly, 51(4), 483-491.
  • Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. Paper presented at the In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks.
  • Romero, J. R., Roncallo, P. F., Akkiraju, P. C., Ponzoni, I., Echenique, V. C., & Carballido, J. A. (2013). Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires. Computers and Electronics in Agriculture, 96, 173-179.
  • Schmid, H. (1994). Part-of-speech tagging with neural networks. Paper presented at the In Proceedings of the International Conference on Computational Linguistics, pages 172-176.
  • Tian, G., Lu, L., & McIntosh, C. (2021). What factors affect consumers’ dining sentiments and their ratings: Evidence from restaurant online review data. Food Quality and Preference, 88, 104060.
  • Zahoor, K., Bawany, N. Z., & Hamid, S. (2020). Sentiment Analysis and Classification of Restaurant Reviews using Machine Learning. Paper presented at the 2020 21st International Arab Conference on Information Technology (ACIT).
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Turizm (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Yusuf Durmuş 0000-0001-8286-4141

Erken Görünüm Tarihi 23 Mart 2022
Yayımlanma Tarihi 29 Temmuz 2022
Gönderilme Tarihi 19 Mart 2022
Kabul Tarihi 24 Nisan 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 5 Sayı: 2

Kaynak Göster

APA Durmuş, Y. (2022). Sentiment Analysis of Restaurant Reviews in Artvin Province by Rule-based Sentiment Analysis and Machine Learning. GSI Journals Serie A: Advancements in Tourism Recreation and Sports Sciences, 5(2), 134-144. https://doi.org/10.53353/atrss.1090401
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