以面向為基礎之旅館評論情感分析—基於建構輔助句之Bert句對分類技術

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李湘琪(Hsiang-Chi Lee) 查詢紙本館藏

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論文名稱

以面向為基礎之旅館評論情感分析—基於建構輔助句之Bert句對分類技術
(Aspect-based Sentiment Analysis for Hotel Reviews – A Bert Sentence-pair Classification Approach Using Auxiliary Sentences)

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至系統瀏覽論文 (2028-8-1以後開放)

摘要(中)

旅遊業是規模龐大且成長速度仍不斷增加的產業之一，其中旅宿業與旅遊業之間緊密相關，因此如何帶動旅宿業的績效成長是當今熱切關注的議題。隨著社交媒體和線上平台的普及，越來越多消費者在網路上表達自己的意見和情緒，因此線上評論的分析對於業者至關重要。透過線上旅館評論之情感分析能有效地量化消費者在各面向的顧客體驗和輿論，讓企業能夠更加深入地了解消費者的需求和心理，從而提升服務品質。過去研究較少針對中文旅館評論進行細粒度情感分析。因此，本研究旨在利用基於建構輔助句的Bert句對分類技術Bert-pair來對中文旅館評論進行面向級情感分析。此外，本研究進一步提出一種新的自動建構輔助句之方法Bert-pair-AA，其透過主題關鍵字技術及文本相似度等技術來自動捕捉句子之隱含意義，以提升模型之預測效能。研究結果顯示，在實驗1中，Bert-pair方法優於傳統的區分子任務方法，並可大幅提升模型預測的準確性，整體Macro-F1進步約為41.1%。而在實驗2中，本研究所設計的Bert-pair-AA方法略優於Bert-pair方法，整體Macro-F1進步約2.1%。這些結果表明採用Bert-pair方法可以提高中文旅館評論情感分析的準確性，而Bert-pair-AA方法進一步優化了該方法的效果。以上發現能為旅宿業提供更準確和實用的情感分析工具，有助於提升客戶體驗和業務效益。

摘要(英)

The tourism industry is one of the largest and continuously growing industries, with a close connection between the accommodation sector and the overall tourism sector. Thus, driving performance growth in the accommodation industry is a pressing concern. With the popularity of social media and online platforms, an increasing number of consumers express their opinions and sentiments online, so analyzing online reviews is crucial for businesses. Utilizing sentiment analysis of online hotel reviews quantifies customer experiences and opinions, enabling enterprises to better understand consumer needs and preferences, and improve service quality. Previous studies have focused less on aspect-based sentiment analysis of Chinese hotel reviews. Therefore, this study aims to use the Bert-pair technique based on constructing auxiliary sentences to perform aspect-based sentiment analysis on Chinese hotel reviews. Additionally, a novel method called Bert-pair-AA is proposed, which automatically constructs auxiliary sentences to enhance the model’s predictive performance. This method captures the implied meanings of sentences using techniques such as topic keywords and text similarity. Experimental results indicate that in Experiment 1, the Bert-pair method outperforms traditional classification methods, significantly improving the accuracy of the model’s predictions with an overall Macro-F1 improvement of approximately 41.1%. In Experiment 2, the proposed Bert-pair-AA method slightly outperforms the Bert-pair method, achieving an overall Macro-F1 improvement of approximately 2.1%. These findings demonstrate that adopting the Bert-pair method can enhance the accuracy of sentiment analysis for Chinese hotel reviews, and the Bert-pair-AA method further optimizes its effectiveness. These findings offer more accurate sentiment analysis tools for the accommodation industry, leading to improved customer experiences and business efficiency.

關鍵字(中)

★ 旅館評論
★ 面向級情感分析
★ Bert
★ 輔助句
★ 機器學習

關鍵字(英)

★ Hotel reviews
★ aspect-based sentiment analysis
★ Bert
★ auxiliary sentence
★ machine learning

論文目次

第一章緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
第二章文獻探討 6
2.1 ABSA任務 6
2.2 旅館與其他領域資料集於ABSA之相關研究 10
第三章研究方法 12
3.1 資料來源和說明 14
3.2 資料前處理 15
3.3 Bert-pair模型技術 16
3.4 Bert-pair-AA輔助句建構方法設計 18
3.4.1 六個面向代表字詞 18
3.4.2 自動建構輔助句 19
3.5 預測模型評估指標 24
第四章實驗評估 25
4.1 實驗設計與分析技術 25
4.1.1 實驗 1 25
4.1.2 實驗 2 29
4.2 實驗結果 30
4.2.1 實驗 1 30
4.2.2 實驗 2 33
4.3 討論 35
第五章研究結論與建議 41
5.1 研究結論 41
5.2 研究限制 42
5.3 未來研究方向與建議 43
參考文獻 44
附錄一 52

參考文獻

[1] Ahmed, M., Pan, S., Su, J., Cao, X., Zhang, W., Wen, B., & Liu, Y. (2022). BERT-ASC: Implicit Aspect Representation Learning through Auxiliary-Sentence Construction for Sentiment Analysis (arXiv:2203.11702). arXiv. https://doi.org/10.48550/arXiv.2203.11702
[2] Akehurst, G. (2008). User generated content: The use of blogs for tourism organisations and tourism consumers. Service Business, 3(1), 51. https://doi.org/10.1007/s11628-008-0054-2
[3] Akhtar, N., Zubair, N., Kumar, A., & Ahmad, T. (2017). Aspect based Sentiment Oriented Summarization of Hotel Reviews. Procedia Computer Science, 115, 563–571. https://doi.org/10.1016/j.procs.2017.09.115
[4] Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y., & Qawasmeh, O. (2019). Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ reviews using morphological, syntactic and semantic features. Information Processing & Management, 56(2), 308–319. https://doi.org/10.1016/j.ipm.2018.01.006
[5] Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020). The future of social media in marketing. Journal of the Academy of Marketing Science, 48(1), 79–95. https://doi.org/10.1007/s11747-019-00695-1
[6] Azhar, A. N., Khodra, M. L., & Sutiono, A. P. (2019). Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting. 2019 International Conference on Electrical Engineering and Informatics (ICEEI), 35–40. https://doi.org/10.1109/ICEEI47359.2019.8988898
[7] Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
[8] Chang, Y.-C., Ku, C.-H., & Chen, C.-H. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management, 48, 263–279. https://doi.org/10.1016/j.ijinfomgt.2017.11.001
[9] Chen, F., & Huang, Y. (2019). Knowledge-enhanced neural networks for sentiment analysis of Chinese reviews. Neurocomputing, 368, 51–58. https://doi.org/10.1016/j.neucom.2019.08.054
[10] Colicev, A., Kumar, A., & O’Connor, P. (2019). Modeling the relationship between firm and user generated content and the stages of the marketing funnel. International Journal of Research in Marketing, 36(1), 100–116. https://doi.org/10.1016/j.ijresmar.2018.09.005
[11] Daugherty, T., Eastin, M. S., & Bright, L. (2008). Exploring Consumer Motivations for Creating User-Generated Content. Journal of Interactive Advertising, 8(2), 16–25. https://doi.org/10.1080/15252019.2008.10722139
[12] Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-1423
[13] Ganu, G., Elhadad, N., & Marian, A. (2009). Beyond the Stars: Improving Rating Predictions using Review Text Content. International Workshop on the Web and Databases. https://www.semanticscholar.org/paper/Beyond-the-Stars%3A-Improving-Rating-Predictions-Text-Ganu-Elhadad/1b41abbf9d3707a1a5c0fcf8e1f7734da0e61703
[14] Glaveli, N., Manolitzas, P., Palamas, S., Grigoroudis, E., & Zopounidis, C. (2023). Developing effective strategic decision-making in the areas of hotel quality management and customer satisfaction from online ratings. Current Issues in Tourism, 26(6), 1003–1021. https://doi.org/10.1080/13683500.2022.2048805
[15] Hollebeek, L. D., & Macky, K. (2019). Digital Content Marketing’s Role in Fostering Consumer Engagement, Trust, and Value: Framework, Fundamental Propositions, and Implications. Journal of Interactive Marketing, 45(1), 27–41. https://doi.org/10.1016/j.intmar.2018.07.003
[16] Jafarian, H., Taghavi, A. H., Javaheri, A., & Rawassizadeh, R. (2021). Exploiting BERT to Improve Aspect-Based Sentiment Analysis Performance on Persian Language. 2021 7th International Conference on Web Research (ICWR), 5–8. https://doi.org/10.1109/ICWR51868.2021.9443131
[17] Kim, J. J., & Han, H. (2022). Saving the hotel industry: Strategic response to the COVID-19 pandemic, hotel selection analysis, and customer retention. International Journal of Hospitality Management, 102, 103163. https://doi.org/10.1016/j.ijhm.2022.103163
[18] Kiritchenko, S., Zhu, X., Cherry, C., & Mohammad, S. (2014). NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 437–442. https://doi.org/10.3115/v1/S14-2076
[19] Lai, X., Wang, F., & Wang, X. (2021). Asymmetric relationship between customer sentiment and online hotel ratings: The moderating effects of review characteristics. International Journal of Contemporary Hospitality Management, 33(6), 2137–2156. https://doi.org/10.1108/IJCHM-07-2020-0708
[20] Lam, J. M. S., Ismail, H., & Lee, S. (2020). From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. Journal of Destination Marketing & Management, 18, 100490. https://doi.org/10.1016/j.jdmm.2020.100490
[21] Leposa, A. (2019). Stats: Travel Industry Second-Fastest Growing Sector in the World. Travel Agent Central. https://www.travelagentcentral.com/running-your-business/stats-travel-industry-second-fastest-growing-sector-world
[22] Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49(1), 51–70. https://doi.org/10.1007/s11747-020-00733-3
[23] Li, H., Ye, Q., & Law, R. (2013). Determinants of Customer Satisfaction in the Hotel Industry: An Application of Online Review Analysis. Asia Pacific Journal of Tourism Research, 18(7), 784–802. https://doi.org/10.1080/10941665.2012.708351
[24] Liao, W., Zeng, B., Yin, X., & Wei, P. (2021). An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa. Applied Intelligence, 51(6), 3522–3533. https://doi.org/10.1007/s10489-020-01964-1
[25] Lin, Y.-C., & Chen, C.-M. (2022). How do hotel characteristics moderate the impact of COVID-19 on hotel performance? Evidence from Taiwan. Current Issues in Tourism, 25(8), 1192–1197. https://doi.org/10.1080/13683500.2021.1910213
[26] Liu, B. (2012). Sentiment Analysis and Opinion Mining. Springer International Publishing. https://doi.org/10.1007/978-3-031-02145-9
[27] Liu, J., Teng, Z., Cui, L., Liu, H., & Zhang, Y. (2021). Solving Aspect Category Sentiment Analysis as a Text Generation Task. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 4406–4416. https://doi.org/10.18653/v1/2021.emnlp-main.361
[28] Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. https://openreview.net/forum?id=SyxS0T4tvS
[29] Lu, W., & Stepchenkova, S. (2015). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119–154. https://doi.org/10.1080/19368623.2014.907758
[30] Luo, J., Huang, S. (Sam), & Wang, R. (2021). A fine-grained sentiment analysis of online guest reviews of economy hotels in China. Journal of Hospitality Marketing & Management, 30(1), 71–95. https://doi.org/10.1080/19368623.2020.1772163
[31] Ma, D., Li, S., Zhang, X., & Wang, H. (2017). Interactive Attention Networks for Aspect-Level Sentiment Classification. 4068–4074.
[32] Maks, I., & Vossen, P. (2012). A lexicon model for deep sentiment analysis and opinion mining applications. Decision Support Systems, 53(4), 680–688. https://doi.org/10.1016/j.dss.2012.05.025
[33] Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
[34] Miao, L., Im, J., So, K. K. F., & Cao, Y. (2022). Post-pandemic and post-traumatic tourism behavior. Annals of Tourism Research, 95, 103410. https://doi.org/10.1016/j.annals.2022.103410
[35] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space (arXiv:1301.3781). arXiv. https://doi.org/10.48550/arXiv.1301.3781
[36] Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep Learning--based Text Classification: A Comprehensive Review. ACM Computing Surveys, 54(3), 62:1-62:40. https://doi.org/10.1145/3439726
[37] Mostafa, L. (2020). Machine Learning-Based Sentiment Analysis for Analyzing the Travelers Reviews on Egyptian Hotels. A.-E. Hassanien, A. T. Azar, T. Gaber, D. Oliva, & F. M. Tolba, Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (pp. 405–413). Springer International Publishing. https://doi.org/10.1007/978-3-030-44289-7_38
[38] Movahedi, S., Ghadery, E., Faili, H., & Shakery, A. (2019). Aspect Category Detection via Topic-Attention Network (arXiv:1901.01183). arXiv. http://arxiv.org/abs/1901.01183
[39] Narangajavana Kaosiri, Y., Callarisa Fiol, L. J., Moliner Tena, M. Á., Rodríguez Artola, R. M., & Sánchez García, J. (2019). User-Generated Content Sources in Social Media: A New Approach to Explore Tourist Satisfaction. Journal of Travel Research, 58(2), 253–265. https://doi.org/10.1177/0047287517746014
[40] Nazir, A., Rao, Y., Wu, L., & Sun, L. (2022). Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey. IEEE Transactions on Affective Computing, 13(2), 845–863. https://doi.org/10.1109/TAFFC.2020.2970399
[41] Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1–15. https://doi.org/10.1080/24751839.2020.1790793
[42] OECD. (2020). OECD Tourism Trends and Policies 2020. OECD. https://doi.org/10.1787/6b47b985-en
[43] Oh, H., & Parks, S. C. (1996). Customer Satisfaction and Service Quality: A Critical Review of the Literature and Research Implications for the Hospitality Industry. Hospitality Research Journal, 20(3), 35–64. https://doi.org/10.1177/109634809602000303
[44] Palakvangsa-Na-Ayudhya, S., Sriarunrungreung, V., Thongprasan, P., & Porcharoen, S. (2011). Nebular: A sentiment classification system for the tourism business. 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE), 293–298. https://doi.org/10.1109/JCSSE.2011.5930137
[45] Pathak, A., Kumar, S., Roy, P. P., & Kim, B.-G. (2021). Aspect-Based Sentiment Analysis in Hindi Language by Ensembling Pre-Trained mBERT Models. Electronics, 10(21), Article 21. https://doi.org/10.3390/electronics10212641
[46] Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. https://doi.org/10.3115/v1/D14-1162
[47] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., & Manandhar, S. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 27–35. https://doi.org/10.3115/v1/S14-2004
[48] Priyantina, R., & Sarno, R. (2019). Sentiment Analysis of Hotel Reviews Using Latent Dirichlet Allocation, Semantic Similarity and LSTM. International Journal of Intelligent Engineering and Systems, 12, 142–155. https://doi.org/10.22266/ijies2019.0831.14
[49] Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 1 - EMNLP ’09, 1, 248. https://doi.org/10.3115/1699510.1699543
[50] Ray, B., Garain, A., & Sarkar, R. (2021). An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Applied Soft Computing, 98, 106935. https://doi.org/10.1016/j.asoc.2020.106935
[51] Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813–830. https://doi.org/10.1109/TKDE.2015.2485209
[52] Sharpley, R. (2000). The influence of the accommodation sector on tourism development: Lessons from Cyprus. International Journal of Hospitality Management, 19(3), 275–293. https://doi.org/10.1016/S0278-4319(00)00021-9
[53] Shen, H., Zhao, C., Fan, D. X. F., & Buhalis, D. (2022). The effect of hotel livestreaming on viewers’ purchase intention: Exploring the role of parasocial interaction and emotional engagement. International Journal of Hospitality Management, 107, 103348. https://doi.org/10.1016/j.ijhm.2022.103348
[54] Sun, C., Huang, L., & Qiu, X. (2019). Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (arXiv:1903.09588). arXiv. https://doi.org/10.48550/arXiv.1903.09588
[55] Tang, D., Qin, B., Feng, X., & Liu, T. (2016). Effective LSTMs for Target-Dependent Sentiment Classification. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 3298–3307. https://aclanthology.org/C16-1311
[56] Tang, D., Qin, B., & Liu, T. (2016). Aspect Level Sentiment Classification with Deep Memory Network. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 214–224. https://doi.org/10.18653/v1/D16-1021
[57] Tulkens, S., & van Cranenburgh, A. (2020). Embarrassingly Simple Unsupervised Aspect Extraction. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 3182–3187. https://doi.org/10.18653/v1/2020.acl-main.290
[58] Wagner, J., Arora, P., Cortes, S., Barman, U., Bogdanova, D., Foster, J., & Tounsi, L. (2014). DCU: Aspect-based Polarity Classification for SemEval Task 4. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 223–229. https://doi.org/10.3115/v1/S14-2036
[59] Wang, Y., Huang, M., Zhu, X., & Zhao, L. (2016). Attention-based LSTM for Aspect-level Sentiment Classification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 606–615. https://doi.org/10.18653/v1/D16-1058
[60] World Tourism Organization (UNWTO). (2018). UNWTO Tourism Highlights: 2018 Edition. World Tourism Organization (UNWTO). https://doi.org/10.18111/9789284419876
[61] World Tourism Organization (UNWTO). (2021). The Economic Contribution of Tourism and the Impact of COVID-19. World Tourism Organization (UNWTO). https://doi.org/10.18111/9789284423200
[62] Wu, Z., & Ong, D. C. (2020). Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis (arXiv:2010.07523). arXiv. https://doi.org/10.48550/arXiv.2010.07523
[63] Xenos, D., Theodorakakos, P., Pavlopoulos, J., Malakasiotis, P., & Androutsopoulos, I. (2016). AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis. 312–317. https://doi.org/10.18653/v1/S16-1050
[64] Xianghua, F., Guo, L., Yanyan, G., & Zhiqiang, W. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon. Knowledge-Based Systems, 37, 186–195. https://doi.org/10.1016/j.knosys.2012.08.003
[65] Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019). BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (arXiv:1904.02232). arXiv. https://doi.org/10.48550/arXiv.1904.02232
[66] Xue, W., & Li, T. (2018). Aspect Based Sentiment Analysis with Gated Convolutional Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2514–2523. https://doi.org/10.18653/v1/P18-1234
[67] Yang, M., Yin, W., Qu, Q., Tu, W., Shen, Y., & Chen, X. (2021). Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification. IEEE Transactions on Affective Computing, 12(3), 761–775. https://doi.org/10.1109/TAFFC.2019.2897093
[68] Yu, L.-C., Wu, J.-L., Chang, P.-C., & Chu, H.-S. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems, 41, 89–97. https://doi.org/10.1016/j.knosys.2013.01.001
[69] Yu, S., Su, J., & Luo, D. (2019). Improving BERT-Based Text Classification With Auxiliary Sentence and Domain Knowledge. IEEE Access, 7, 176600–176612. https://doi.org/10.1109/ACCESS.2019.2953990
[70] Zhang, W., Li, X., Deng, Y., Bing, L., & Lam, W. (2022). A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges (arXiv:2203.01054). arXiv. https://doi.org/10.48550/arXiv.2203.01054
[71] Zhou, X., Wan, X., & Xiao, J. (2015). Representation Learning for Aspect Category Detection in Online Reviews. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1), Article 1. https://doi.org/10.1609/aaai.v29i1.9194

指導教授

胡雅涵(Ya-Han Hu)

審核日期

2023-6-30

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