A template approach for summarizing restaurant reviews

以作者查詢圖書館館藏

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姓名

甘哲宇(Jer-Yeu Gan) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(A template approach for summarizing restaurant reviews)

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至系統瀏覽論文 (2025-6-27以後開放)

摘要(中)

在社群網路發展越來越快的時代下，使用者們在餐廳評論網站上的評論也逐漸增加，為了要讓使用者可以更快速的了解評論網站上的評論資訊，本論文會實作一種基於模板、主題和情感的餐廳評論摘要化的模板系統。本論文還使用了預定義主題的概念，將評論摘要依照主題來放，因此可以讓使用者一看就非常清楚及明瞭。在評估時會依照資訊豐富度(informativeness)、清晰度(clearness) 、有用性(helpfulness)與Refresh和Gensim的系統在比較，來讓使用者主觀選擇較好的模板。最後，我們發現我們的方法在資訊豐富度和有用性方面優於其他兩種方法。這個結果證明我們的方法可以提供更多的訊息，對用戶有更大的幫助。

摘要(英)

In the era of rapid development of social networks, user reviews on restaurant review sites have increased rapidly. In order to enable users to more quickly grasp the focus of the review information on the review site, this article will implement a template method for summarizing restaurant reviews, which is based on templates, topics, and emotions. This article also uses the concept of pre-defined topics applicable to restaurants to summarize reviews so that users can understand the reviews more clearly and accurately. In the evaluation, we compared the template method with the Refresh and Gensim systems according to the criteria of informativeness, clarity and usefulness to evaluate which method can better satisfy the user′s subjective preferences. Finally, we found that our method is superior to the other two methods in terms of informativeness and usefulness. This result proves that our method can provide more information and is more helpful to users.

關鍵字(中)

★ 摘要
★ 餐廳評論
★ 模板
★ 情緒分析
★ TextRank

關鍵字(英)

★ Summarization
★ Restaurant reviews
★ Template
★ Template
★ TextRank

論文目次

摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
Chapter 1 Introduction 1
Chapter 2 Related Work 6
2.1 Text Summarization Approaches 6
2.2 Applications of Text Summarization 8
2.3 Text Summarization in Restaurant Reviews 9
2.4 Predefined Topics 10
2.5 LDA Review 11
Chapter 3 Methodology 13
3.1 Find k keywords for a given topic 13
3.2 Training a classification model with 4k keywords 14
3.3 Find the topic of each word in the sentence 18
3.4 Label Sentences with topic tags 20
3.5 Calculate the sentiment score of each sentence 23
3.6 Calculate the sentiment of each topic 24
3.7 Find the most representative positive and negative sentences for each topic 25
3.8 Create the template 27
Chapter 4 Evaluation 28
4.1 Datasets 28
4.2 Experiment Design 28
4.3 Experiment Results 30
Chapter 5 Conclusions 34
Reference 35
Appendix 1：50 keywords in four topics 39
Appendix 2：Template of 5 Napkin Burger 41
Appendix 3：Refresh method of 5 Napkin Burger 42
Appendix 4：Gensim method of 5 Napkin Burger 43
Appendix 5：Template of Applebee′s 44
Appendix 6：Refresh method of Applebee′s 45
Appendix 7：Gensim method of Applebee′s 46
Appendix 8：Template of Bea′s of Bloomsbury 47
Appendix 9：Refresh method of Bea′s of Bloomsbury 48
Appendix 10：Gensim method of Bea′s of Bloomsbury 49
Appendix 11：Template of Serafina 77th 50
Appendix 12：Refresh method of Serafina 77th 51
Appendix 13：Gensim method of Serafina 77th 52

參考文獻

Akhtar, N., Zubair, N., Kumar, A., & Ahmad, T. J. P. c. s. (2017). Aspect based sentiment oriented summarization of hotel reviews. 115, 563-571.
Blei, D. M., Ng, A. Y., & Jordan, M. I. J. J. o. m. L. r. (2003). Latent dirichlet allocation. 3(Jan), 993-1022.
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine.
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. J. I. I. s. (2013). New avenues in opinion mining and sentiment analysis. 28(2), 15-21.
Di Fabbrizio, G., Aker, A., & Gaizauskas, R. J. I. I. S. (2013). Summarizing online reviews using aspect rating distributions and language modeling. 28(3), 28-37.
Fachrurrozi, M., Yusliani, N., & Yoanita, R. U. (2013). Frequent term based text summarization for bahasa indonesia.
Gaikwad, D. K., Mahender, C. N. J. I. J. o. A. R. i. C., & Engineering, C. (2016). A review paper on text summarization. 5(3), 154-160.
Gambhir, M., & Gupta, V. (2016). Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 47(1), 1-66. doi:10.1007/s10462-016-9475-9
Gerani, S., Mehdad, Y., Carenini, G., Ng, R., & Nejat, B. (2014). Abstractive summarization of product reviews using discourse structure. Paper presented at the Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP).
Goyal, P., Goel, S., & Sethia, K. (2015). Text summarization for wikipedia articles. In.
Guo, Y., Barnes, S. J., & Jia, Q. (2017). Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tourism Management, 59, 467-483. doi:10.1016/j.tourman.2016.09.009
Gupta, V., & Lehal, G. S. (2010). A Survey of Text Summarization Extractive Techniques. Journal of Emerging Technologies in Web Intelligence, 2(3). doi:10.4304/jetwi.2.3.258-268
Heu, J.-U., Qasim, I., & Lee, D.-H. (2015). FoDoSu: Multi-document summarization exploiting semantic analysis based on social Folksonomy. Information Processing & Management, 51(1), 212-225. doi:10.1016/j.ipm.2014.06.003
Hingu, D., Shah, D., & Udmale, S. S. (2015). Automatic text summarization of wikipedia articles. Paper presented at the 2015 International Conference on Communication, Information & Computing Technology (ICCICT).
Hovy, E., & Lin, C.-Y. J. A. i. a. t. s. (1999). Automated text summarization in SUMMARIST. 14.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
Hu, Y.-H., Chen, Y.-L., & Chou, H.-L. (2017). Opinion mining from online hotel reviews – A text summarization approach. Information Processing & Management, 53(2), 436-449. doi:10.1016/j.ipm.2016.12.002
Hutto, C. J., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Paper presented at the Eighth international AAAI conference on weblogs and social media.
Jagarlamudi, J., Daumé III, H., & Udupa, R. (2012). Incorporating lexical priors into topic models. Paper presented at the Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics.
Kar, M., Nunes, S., & Ribeiro, C. (2015). Summarization of changes in dynamic text collections using Latent Dirichlet Allocation model. Information Processing & Management, 51(6), 809-833. doi:10.1016/j.ipm.2015.06.002
Kikuchi, Y., Hirao, T., Takamura, H., Okumura, M., & Nagata, M. (2014). Single document summarization based on nested tree structure. Paper presented at the Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).
Kleinberg, J. M. J. J. o. t. A. (1999). Authoritative sources in a hyperlinked environment. 46(5), 604-632.
Kumar, Y. J., Goh, O. S., Basiron, H., Choon, N. H., & Suppiah, P. C. (2016). A Review on Automatic Text Summarization Approaches. Journal of Computer Science, 12(4), 178-190. doi:10.3844/jcssp.2016.178.190
Lee, A. J. T., Yang, F.-C., Chen, C.-H., Wang, C.-S., & Sun, C.-Y. (2016). Mining perceptual maps from consumer reviews. Decision Support Systems, 82, 12-25. doi:10.1016/j.dss.2015.11.002
Litvak, M., & Last, M. (2008). Graph-based keyword extraction for single-document summarization. Paper presented at the Proceedings of the workshop on Multi-source Multilingual Information Extraction and Summarization.
Liu, C.-L., Hsaio, W.-H., Lee, C.-H., Lu, G.-C., & Jou, E. (2012). Movie Rating and Review Summarization in Mobile Environment. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(3), 397-407. doi:10.1109/tsmcc.2011.2136334
Lommel, L., Riebling, M., Funk, B., & Junginger, C. (2019). Topic Embeddings–A New Approach to Classify Very Short Documents Based on Predefined Topics.
Mani, I. (2001). Summarization evaluation: An overview.
Mihalcea, R. (2004). Graph-based ranking algorithms for sentence extraction, applied to text summarization. Paper presented at the Proceedings of the ACL Interactive Poster and Demonstration Sessions.
Mihalcea, R., & Tarau, P. (2004). Textrank: Bringing order into text. Paper presented at the Proceedings of the 2004 conference on empirical methods in natural language processing.
Narayan, S., Cohen, S. B., & Lapata, M. J. a. p. a. (2018). Ranking sentences for extractive summarization with reinforcement learning.
Nguyen, P., Mahajan, M., & Zweig, G. J. M. R., Redmond, WA, Citeseer. (2007). Summarization of multiple user reviews in the restaurant domain.
Oya, T., Mehdad, Y., Carenini, G., & Ng, R. (2014). A template-based abstractive meeting summarization: Leveraging summary and source text relationships. Paper presented at the Proceedings of the 8th International Natural Language Generation Conference (INLG).
Plaza, L., Diaz, A., & Gervas, P. (2011). A semantic graph-based approach to biomedical summarisation. Artif Intell Med, 53(1), 1-14. doi:10.1016/j.artmed.2011.06.005
Ramesh, A., Goldwasser, D., Huang, B., Daumé III, H., & Getoor, L. (2014). Understanding MOOC discussion forums using seeded LDA. Paper presented at the Proceedings of the ninth workshop on innovative use of NLP for building educational applications.
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.
Roul, R. K., Mehrotra, S., Pungaliya, Y., & Sahoo, J. K. (2019). A New Automatic Multi-document Text Summarization using Topic Modeling. In Distributed Computing and Internet Technology (pp. 212-221).
Roy, A., Guria, S., Halder, S., Banerjee, S., & Mandal, S. (2018). Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations. International Journal of Synthetic Emotions, 9(2), 111-120. doi:10.4018/ijse.2018070107
Siva kumar, A. P., Premchand, P., & Govardhan, A. (2011). Query-Based Summarizer Based on Similarity of Sentences and Word Frequency. International Journal of Data Mining & Knowledge Management Process, 1(3), 1-12. doi:10.5121/ijdkp.2011.1301
Somprasertsri, G., & Lalitrojwong, P. J. J. U. (2010). Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization. 16(6), 938-955.
Trappey, A. J. C., Trappey, C. V., & Wu, C.-Y. (2009). Automatic patent document summarization for collaborative knowledge systems and services. Journal of Systems Science and Systems Engineering, 18(1), 71-94. doi:10.1007/s11518-009-5100-7
Tsatsaronis, G., Varlamis, I., & Nørvåg, K. (2010). SemanticRank: ranking keywords and sentences using semantic graphs. Paper presented at the Proceedings of the 23rd International Conference on Computational Linguistics.
Wang, D., Zhu, S., & Li, T. (2013). SumView: A Web-based engine for summarizing product reviews and customer opinions. Expert Systems with Applications, 40(1), 27-33. doi:10.1016/j.eswa.2012.05.070
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., . . . Patwardhan, S. (2005). OpinionFinder. Paper presented at the Proceedings of HLT/EMNLP on Interactive Demonstrations -.
Wu, P., Zhou, Q., Lei, Z., Qiu, W., & Li, X. (2018). Template Oriented Text Summarization via Knowledge Graph. Paper presented at the 2018 International Conference on Audio, Language and Image Processing (ICALIP).
Xu, X., Meng, T., & Cheng, X. (2011). Aspect-based extractive summarization of online reviews. Paper presented at the Proceedings of the 2011 ACM Symposium on Applied Computing.
Yadav, C. S., Sharan, A., Kumar, R., & Biswas, P. (2016). A New Approach for Single Text Document Summarization. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 401-411).
Yeh, J.-Y., Ke, H.-R., & Yang, W.-P. J. E. S. w. A. (2008). iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network. 35(3), 1451-1462.
Zhang, Z., Ye, Q., Zhang, Z., & Li, Y. (2011). Sentiment classification of Internet restaurant reviews written in Cantonese. Expert Systems with Applications, 38(6), 7674-7682. doi:10.1016/j.eswa.2010.12.147
Zhuang, L., Jing, F., & Zhu, X.-Y. (2006). Movie review mining and summarization. Paper presented at the Proceedings of the 15th ACM international conference on Information and knowledge management.

指導教授

陳彥良(Yen-Liang Chen)

審核日期

2020-6-24

推文