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Restoran Müşterilerinin Geri Bildirimleri Üzerinde Hedef Kategorinin Tespiti ve Hedef Tabanlı Duygu Analizi

Year 2023, Volume: 14 Issue: 40, 1205 - 1221, 25.11.2023

Murat Fatih Tuna Mesut Polatgil Oğuz Kaynar

https://doi.org/10.21076/vizyoner.1208355

Abstract

Günümüzde tüketicilerin ürün ve hizmetler konusunda fikir paylaşabilecekleri birçok mecra bulunmaktadır. Bu fikirler, geri bildirimin yapısı itibariyle genellikle metin formatındadır. Duygu analizi, metin tabanlı bilgi kaynaklarında son yıllarda önem kazanan bir konudur. Daha hassas bir duygu analiz türü olan Hedef Tabanlı Duygu Analizi bir cümle içerisinde hedef terim, hedef kategori ve duygu sınıfının belirlenmesi işidir. Bu çalışmada Semeval ABSA yarışmasında yarışmacılara sunulan restoran müşterilerine ait yorumlardan oluşan bir veri seti kullanılmıştır. Word2vec, Glove, Fastext ve Bert yöntemleri kullanılarak veri seti üzerinde hedef terim, hedef kategori ve duygu sınıfının belirlenmesi işlemi gerçekleştirilmiştir. Kelimeyi vektörü ile cümle vektörünün birleştirilmesi ABSA için sınıflandırma başarısını artırıp artıramayacağı hipotezi test edilmiştir. Dört farklı vektör yöntemi ile yapılan sınıflandırmada hedef terim için 0,78 F1 skoru ile Fasttext yöntemi, hedef kategori için 0,57 F1 skoru ile Fasttext ve duygu sınıfı için 0,76 F1 skoru ile Bert yöntemi en başarılı sonuçları vermiştir. Bu sonuçlar literatürde farklı veri setleri ve farklı diller için yapılan çalışmalarla kıyaslanmıştır. Sonuç olarak Fasttext ve Bert temsil yöntemlerinin hedef tabanlı Türkçe dilindeki metinlerin duygu analizinde başarılı sonuçlar verdiği tespit edilmiştir.

Keywords

Restoran yorumları Hedef Tabanlı Duygu Analizi Fasttext Bert

References

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  • Akın, M. D. ve Akın, A. A. (2007). Türk dilleri için açık kaynaklı doğal dil işleme kütüphanesi: ZEMBEREK. Elektrik mühendisliği, 431, 38-44.
  • Barger, V., Peltier, J. W. ve Schultz, D. E. (2016). Social media and consumer engagement: A review and research agenda. Journal of Research in Interactive Marketing, 10(4), 268-287.
  • Bayraktar, K., Yavanoglu, U. ve Ozbilen, A. (2019). A rule-based holistic approach for Turkish aspect-based sentiment analysis. 2019 IEEE International Conference on Big Data (s. 2154-2158). Los Angeles, CA, USA.
  • Berger, J. ve Iyengar, R. (2013). Communication channels and word of mouth: how the medium shapes the message. Journal of Consumer Research, 40(3), 567-579.
  • Brun, C., Popa, D. N. ve Roux, C. (2014). XRCE: hybrid classification for aspect-based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 838-842). Dublin, Ireland.
  • Castellucci, G., Filice, S., Croce, D. ve Basili, R. (2014). UNITOR: aspect based sentiment analysis with structured learning. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 761-767). Dublin, Ireland.
  • Chen, P.-Y., Wu, S. ve Yoon, J. (2004). The impact of online recommendations and consumer feedback on Sales. ICIS 2004 Proceedings (s. 711–724). Washington, DC, USA.
  • Chen, Z. ve Qian, T. (2020). Enhancing aspect term extraction with soft prototypes. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (s. 2107-2117). Punta Cana, Dominican Republic.
  • Coşgun, E., Çelebi, A. ve Güllü, M. K. (3-5 Ekim 2019). Dengesiz Veri Kümeleri İçin Epileptik Nöbet Tahmini, Tıp Teknolojileri Kongresi, Kuşadası/Aydın.
  • Çeti̇n, F. S. ve Eryi̇ği̇t, G. (2018). Türkçe hedef tabanlı duygu analizi için alt görevlerin incelenmesi – hedef terim, hedef kategori ve duygu sınıfı belirleme. Bilişim Teknolojileri Dergisi, 11(1), 43-56.
  • Çiftçi, O. (2022). Türkçe GloVe - Repository for Turkish GloVe word embedding. https://github.com/inzva/Turkish-GloVe adresinden 18 Ekim 2022 tarihinde alınmıştır.
  • de Oliveira Santini, F., Ladeira, W. J., Pinto, D. C., Herter, M. M., Sampaio, C. H. ve Babin, B. J. (2020). Customer engagement in social media: A framework and meta-analysis. Journal of the Academy of Marketing Science, 48(6), 1211-1228.
  • Do, H. H., Prasad, P., Maag, A. ve Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications, 118, 272-299.
  • Fan, Z., Wu, Z., Dai, X.-Y., Huang, S. ve Chen, J. (2019). Target-oriented opinion words extraction with target-fused neural sequence labeling. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume I (s. 2509-2518). Minneapolis, MN, USA.
  • Ghadery, E., Movahedi, S., Jalili Sabet, M., Faili, H. ve Shakery, A. (2019). LICD: A language-independent approach for aspect category detection. L. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff, ve D. Hiemstra (Ed.), Advances in information retrieval içinde (s. 575-589). Springer International Publishing.
  • Giannakopoulos, A., Musat, C., Hossmann, A. ve Baeriswyl, M. (2017). Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (s. 180-188). Copenhagen, Denmark.
  • Grave, E., Bojanowski, P., Gupta, P., Joulin, A ve T. Mikolov (2018). Learning word vectors for 157 languages. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (s. 3483-3487). Miyazaki, Japan.
  • He, R., Lee, W. S., Ng, H. T. ve Dahlmeier, D. (2017). An Unsupervised Neural Attention Model for Aspect Extraction. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-Volume 1 (s. 388-397). Vancouver, Canada.
  • Hercig, T., Brychcín, T., Svoboda, L., Konkol, M., Steinberger, J., Hercig, T., Brychcín, T., Svoboda, L., Konkol, M. ve Steinberger, J. (2016). Unsupervised methods to improve aspect-based sentiment analysis in Czech. Computación y Sistemas, 20(3), 365-375.
  • Kama, B., Ozturk, M., Karagoz, P., Toroslu, I. H. ve Ozay, O. (2016). A web search enhanced feature extraction method for aspect-based sentiment analysis for Turkish informal texts. S. Madria ve T. Hara (Ed.), Big data analytics and knowledge discovery içinde (s. 225-238). Springer International Publishing.
  • Kim, K.-S., Yoo-Lee, E. ve Joanna Sin, S.-C. (2011). Social media as information source: Undergraduates’ use and evaluation behavior. Asis&t, 48(1), 1-3.
  • Köksal, A. (2022). Turkish Pre-trained Word2Vec Model. https://github.com/akoksal/Turkish-Word2Vec adresinden 19 Ekim 2022 tarihinde alınmıştır.
  • Lee, T. Y. ve Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881-894.
  • Li, K., Chen, C., Quan, X., Ling, Q. ve Song, Y. (2020). conditional augmentation for aspect term extraction via masked sequence-to-sequence generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (s. 7056-7066). World Wide Online.
  • Li, X. ve Lam, W. (2017). Deep multi-task learning for aspect term extraction with memory interaction. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (s. 2886-2892). Copenhagen, Denmark.
  • Li, X., Bing, L., Li, P., Lam, W. ve Yang, Z. (2018). Aspect term extraction with history attention and selective transformation. ArXiv:1805.00760, 4194-4200.
  • Liao, M., Li, J., Zhang, H., Wang, L., Wu, X. ve Wong, K.-F. (2019). Coupling global and local context for unsupervised aspect extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (s. 4579-4589). Hong Kong, China.
  • Liu, P., Joty, S. ve Meng, H. (2015). Fine-grained opinion mining with recurrent neural networks and word embeddings. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (s. 1433-1443). Lisbon, Portugal.
  • Ma, D., Li, S., Wu, F., Xie, X. ve Wang, H. (2019). Exploring sequence-to-sequence learning in aspect term extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (s. 3538-3547). Florence, Italy.
  • Mensah, S., Sun, K. ve Aletras, N. (2021). An empirical study on leveraging position embeddings for target-oriented opinion words extraction. arXiv:2109.01238, 9174-9179.
  • Movahedi, S., Ghadery, E., Faili, H. ve Shakery, A. (2019). Aspect category detection via topic-attention network, arXiv:1901.01183, 1-9.
  • Omisakin, O. M., Bandara, C. ve Kularatne, I. (2020). Designing a customer feedback service channel through AI to improve customer satisfaction in the supermarket industry. Journal of Information & Knowledge Management (JIKM), 19(03), 1-34.
  • Ozyurt, B. ve Akcayol, M. A. (2021). A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA. Expert Systems with Applications, 168, 114231.
  • Pang, B., Lee, L. ve Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (s. 79-86). Philadelphia, PA, USA.
  • Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jiménez- Zafra, S. M. ve Eryiğit, G. (2016). SemEval-2016 task 5: Aspect based sentiment analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (s. 19-30). San Diego, CA, USA.
  • Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I. ve Manandhar, S. (2014). SemEval-2014 task 4: Aspect based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 27-35). Dublin, Ireland.
  • Salur, M. U. ve Aydin, İ. (2021). An annotated Turkish aspect based sentiment analysis corpus for smart tourism. Proceedings of Innovations in Intelligent Systems and Applications Conference (s. 1-6). Elazig, Turkey.
  • Salur, M. U., Aydın, İ. ve Jamous, M. (2022). An ensemble approach for aspect term extraction in Turkish texts. Pamukkale University Journal of Engineering Sciences, 28(5), 769-776.
  • Schouten, K. ve Frasincar, F. (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830.
  • SemEval (2016). Task 5: Aspect-Based Sentiment Analysis. https://alt.qcri.org/semeval2016/task5/ adresinden 17 Kasım 2022 tarihinde alınmıştır
  • Shi, T., Li, L., Wang, P. ve Reddy, C. K. (2020). A simple and effective self-supervised contrastive learning framework for aspect detection, arXiv:2009.09107, 1-13.
  • Tulkens, S. ve van Cranenburgh, A. (2020). Embarrassingly simple unsupervised aspect extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (s. 3182-3187. World Wide Online.
  • Veyseh, A. P. B., Nouri, N., Dernoncourt, F., Dou, D. ve Nguyen, T. H. (2020). Introducing syntactic structures into target opinion word extraction with deep learning, arXiv:2010.13378, 8947-8956.
  • Wang, W., Pan, S. J., Dahlmeier, D. ve Xiao, X. (2017). Coupled multi-layer attentions for co-extraction of aspect and opinion terms. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 1-7. San Francisco, CA, USA.
  • Winatmoko, Y. A., Septiandri, A. A. ve Sutiono, A. P. (2020). Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels, arXiv:1909.11879, 1-5.
  • Wu, M., Wang, W. ve Pan, S. J. (2020). Deep weighted maxsat for aspect-based opinion extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (s. 5618-5628). World Wide Online.
  • Xu, H., Liu, B., Shu, L. ve Yu, P. S. (2018). Double embeddings and CNN-based sequence labeling for aspect extraction. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics-Volume 2 (s. 592-598). Melbourne, Australia.
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Detection of Aspect Category and Aspect-Based Sentiment Analysis on Restaurant Customers' Feedbacks

Year 2023, Volume: 14 Issue: 40, 1205 - 1221, 25.11.2023

Murat Fatih Tuna Mesut Polatgil Oğuz Kaynar

https://doi.org/10.21076/vizyoner.1208355

Abstract

Today, there are many channels where consumers can share their ideas about products and services. These opinions are usually in text format due to the nature of the feedback. Sentiment analysis is a topic that has gained importance in recent years, especially in text-based information sources. Aspect-based Sentiment Analysis, which is a more sensitive sentiment analysis technique, is the task of determining the aspect term, aspect category and sentiment class in a sentence. A data set consisting of the comments of restaurant customers presented to the competitors in the Semeval ABSA competition is used in the study. Using Word2vec, Glove, Fastext and Bert methods, the aspect term, aspect category and sentiment class are determined on the data set. The hypothesis is tested whether combining the word vector and the sentence vector can improve classification success for ABSA. In the classification made with four different vector methods, Fasttext method with 0.78 F1 score for the target term, Fasttext with 0.57 F1 score for the target category, and Bert method with 0.76 F1 score for the sentiment class have the most successful results. These results are compared with studies in the literature for different data sets and different languages. As a result, it is determined that Fasttext and Bert representation methods give successful results in sentiment analysis of target-based Turkish language texts.

Keywords

Restaurant Reviews Aspect Based Sentiment Analysis Fasttext Bert

References

  • Ahuja, V. ve Medury, Y. (2010). Corporate blogs as e-CRM tools – building consumer engagement through content management. Journal of Database Marketing & Customer Strategy Management, 17(2), 91-105.
  • Akın, M. D. ve Akın, A. A. (2007). Türk dilleri için açık kaynaklı doğal dil işleme kütüphanesi: ZEMBEREK. Elektrik mühendisliği, 431, 38-44.
  • Barger, V., Peltier, J. W. ve Schultz, D. E. (2016). Social media and consumer engagement: A review and research agenda. Journal of Research in Interactive Marketing, 10(4), 268-287.
  • Bayraktar, K., Yavanoglu, U. ve Ozbilen, A. (2019). A rule-based holistic approach for Turkish aspect-based sentiment analysis. 2019 IEEE International Conference on Big Data (s. 2154-2158). Los Angeles, CA, USA.
  • Berger, J. ve Iyengar, R. (2013). Communication channels and word of mouth: how the medium shapes the message. Journal of Consumer Research, 40(3), 567-579.
  • Brun, C., Popa, D. N. ve Roux, C. (2014). XRCE: hybrid classification for aspect-based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 838-842). Dublin, Ireland.
  • Castellucci, G., Filice, S., Croce, D. ve Basili, R. (2014). UNITOR: aspect based sentiment analysis with structured learning. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 761-767). Dublin, Ireland.
  • Chen, P.-Y., Wu, S. ve Yoon, J. (2004). The impact of online recommendations and consumer feedback on Sales. ICIS 2004 Proceedings (s. 711–724). Washington, DC, USA.
  • Chen, Z. ve Qian, T. (2020). Enhancing aspect term extraction with soft prototypes. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (s. 2107-2117). Punta Cana, Dominican Republic.
  • Coşgun, E., Çelebi, A. ve Güllü, M. K. (3-5 Ekim 2019). Dengesiz Veri Kümeleri İçin Epileptik Nöbet Tahmini, Tıp Teknolojileri Kongresi, Kuşadası/Aydın.
  • Çeti̇n, F. S. ve Eryi̇ği̇t, G. (2018). Türkçe hedef tabanlı duygu analizi için alt görevlerin incelenmesi – hedef terim, hedef kategori ve duygu sınıfı belirleme. Bilişim Teknolojileri Dergisi, 11(1), 43-56.
  • Çiftçi, O. (2022). Türkçe GloVe - Repository for Turkish GloVe word embedding. https://github.com/inzva/Turkish-GloVe adresinden 18 Ekim 2022 tarihinde alınmıştır.
  • de Oliveira Santini, F., Ladeira, W. J., Pinto, D. C., Herter, M. M., Sampaio, C. H. ve Babin, B. J. (2020). Customer engagement in social media: A framework and meta-analysis. Journal of the Academy of Marketing Science, 48(6), 1211-1228.
  • Do, H. H., Prasad, P., Maag, A. ve Alsadoon, A. (2019). Deep learning for aspect-based sentiment analysis: A comparative review. Expert Systems with Applications, 118, 272-299.
  • Fan, Z., Wu, Z., Dai, X.-Y., Huang, S. ve Chen, J. (2019). Target-oriented opinion words extraction with target-fused neural sequence labeling. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume I (s. 2509-2518). Minneapolis, MN, USA.
  • Ghadery, E., Movahedi, S., Jalili Sabet, M., Faili, H. ve Shakery, A. (2019). LICD: A language-independent approach for aspect category detection. L. Azzopardi, B. Stein, N. Fuhr, P. Mayr, C. Hauff, ve D. Hiemstra (Ed.), Advances in information retrieval içinde (s. 575-589). Springer International Publishing.
  • Giannakopoulos, A., Musat, C., Hossmann, A. ve Baeriswyl, M. (2017). Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets. Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (s. 180-188). Copenhagen, Denmark.
  • Grave, E., Bojanowski, P., Gupta, P., Joulin, A ve T. Mikolov (2018). Learning word vectors for 157 languages. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (s. 3483-3487). Miyazaki, Japan.
  • He, R., Lee, W. S., Ng, H. T. ve Dahlmeier, D. (2017). An Unsupervised Neural Attention Model for Aspect Extraction. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics-Volume 1 (s. 388-397). Vancouver, Canada.
  • Hercig, T., Brychcín, T., Svoboda, L., Konkol, M., Steinberger, J., Hercig, T., Brychcín, T., Svoboda, L., Konkol, M. ve Steinberger, J. (2016). Unsupervised methods to improve aspect-based sentiment analysis in Czech. Computación y Sistemas, 20(3), 365-375.
  • Kama, B., Ozturk, M., Karagoz, P., Toroslu, I. H. ve Ozay, O. (2016). A web search enhanced feature extraction method for aspect-based sentiment analysis for Turkish informal texts. S. Madria ve T. Hara (Ed.), Big data analytics and knowledge discovery içinde (s. 225-238). Springer International Publishing.
  • Kim, K.-S., Yoo-Lee, E. ve Joanna Sin, S.-C. (2011). Social media as information source: Undergraduates’ use and evaluation behavior. Asis&t, 48(1), 1-3.
  • Köksal, A. (2022). Turkish Pre-trained Word2Vec Model. https://github.com/akoksal/Turkish-Word2Vec adresinden 19 Ekim 2022 tarihinde alınmıştır.
  • Lee, T. Y. ve Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881-894.
  • Li, K., Chen, C., Quan, X., Ling, Q. ve Song, Y. (2020). conditional augmentation for aspect term extraction via masked sequence-to-sequence generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (s. 7056-7066). World Wide Online.
  • Li, X. ve Lam, W. (2017). Deep multi-task learning for aspect term extraction with memory interaction. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (s. 2886-2892). Copenhagen, Denmark.
  • Li, X., Bing, L., Li, P., Lam, W. ve Yang, Z. (2018). Aspect term extraction with history attention and selective transformation. ArXiv:1805.00760, 4194-4200.
  • Liao, M., Li, J., Zhang, H., Wang, L., Wu, X. ve Wong, K.-F. (2019). Coupling global and local context for unsupervised aspect extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (s. 4579-4589). Hong Kong, China.
  • Liu, P., Joty, S. ve Meng, H. (2015). Fine-grained opinion mining with recurrent neural networks and word embeddings. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (s. 1433-1443). Lisbon, Portugal.
  • Ma, D., Li, S., Wu, F., Xie, X. ve Wang, H. (2019). Exploring sequence-to-sequence learning in aspect term extraction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (s. 3538-3547). Florence, Italy.
  • Mensah, S., Sun, K. ve Aletras, N. (2021). An empirical study on leveraging position embeddings for target-oriented opinion words extraction. arXiv:2109.01238, 9174-9179.
  • Movahedi, S., Ghadery, E., Faili, H. ve Shakery, A. (2019). Aspect category detection via topic-attention network, arXiv:1901.01183, 1-9.
  • Omisakin, O. M., Bandara, C. ve Kularatne, I. (2020). Designing a customer feedback service channel through AI to improve customer satisfaction in the supermarket industry. Journal of Information & Knowledge Management (JIKM), 19(03), 1-34.
  • Ozyurt, B. ve Akcayol, M. A. (2021). A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA. Expert Systems with Applications, 168, 114231.
  • Pang, B., Lee, L. ve Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (s. 79-86). Philadelphia, PA, USA.
  • Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., AL-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., De Clercq, O., Hoste, V., Apidianaki, M., Tannier, X., Loukachevitch, N., Kotelnikov, E., Bel, N., Jiménez- Zafra, S. M. ve Eryiğit, G. (2016). SemEval-2016 task 5: Aspect based sentiment analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (s. 19-30). San Diego, CA, USA.
  • Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I. ve Manandhar, S. (2014). SemEval-2014 task 4: Aspect based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (s. 27-35). Dublin, Ireland.
  • Salur, M. U. ve Aydin, İ. (2021). An annotated Turkish aspect based sentiment analysis corpus for smart tourism. Proceedings of Innovations in Intelligent Systems and Applications Conference (s. 1-6). Elazig, Turkey.
  • Salur, M. U., Aydın, İ. ve Jamous, M. (2022). An ensemble approach for aspect term extraction in Turkish texts. Pamukkale University Journal of Engineering Sciences, 28(5), 769-776.
  • Schouten, K. ve Frasincar, F. (2016). Survey on aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830.
  • SemEval (2016). Task 5: Aspect-Based Sentiment Analysis. https://alt.qcri.org/semeval2016/task5/ adresinden 17 Kasım 2022 tarihinde alınmıştır
  • Shi, T., Li, L., Wang, P. ve Reddy, C. K. (2020). A simple and effective self-supervised contrastive learning framework for aspect detection, arXiv:2009.09107, 1-13.
  • Tulkens, S. ve van Cranenburgh, A. (2020). Embarrassingly simple unsupervised aspect extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (s. 3182-3187. World Wide Online.
  • Veyseh, A. P. B., Nouri, N., Dernoncourt, F., Dou, D. ve Nguyen, T. H. (2020). Introducing syntactic structures into target opinion word extraction with deep learning, arXiv:2010.13378, 8947-8956.
  • Wang, W., Pan, S. J., Dahlmeier, D. ve Xiao, X. (2017). Coupled multi-layer attentions for co-extraction of aspect and opinion terms. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 1-7. San Francisco, CA, USA.
  • Winatmoko, Y. A., Septiandri, A. A. ve Sutiono, A. P. (2020). Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels, arXiv:1909.11879, 1-5.
  • Wu, M., Wang, W. ve Pan, S. J. (2020). Deep weighted maxsat for aspect-based opinion extraction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (s. 5618-5628). World Wide Online.
  • Xu, H., Liu, B., Shu, L. ve Yu, P. S. (2018). Double embeddings and CNN-based sequence labeling for aspect extraction. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics-Volume 2 (s. 592-598). Melbourne, Australia.
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Details

Primary Language Turkish
Subjects Business Administration
Journal Section Research Articles
Authors

Murat Fatih Tuna 0000-0002-8634-8643

Mesut Polatgil 0000-0002-7503-2977

Oğuz Kaynar 0000-0003-2387-4053

Publication Date November 25, 2023
Submission Date November 22, 2022
Published in Issue Year 2023 Volume: 14 Issue: 40

Cite

APA Tuna, M. F., Polatgil, M., & Kaynar, O. (2023). Restoran Müşterilerinin Geri Bildirimleri Üzerinde Hedef Kategorinin Tespiti ve Hedef Tabanlı Duygu Analizi. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 14(40), 1205-1221. https://doi.org/10.21076/vizyoner.1208355
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