Unsupervised tip-mining from customer reviews
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It remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.
“Prior work towards general users investigated extracting experiences [21, 24], tips [11, 41] or snippets suitable for product descriptions [7, 25], yet it remains unknown how reviews can help with the unique interests of BLV users, especially providing descriptive information on product appearance.”
From Revamp: Enhancing Accessible Information Seeking Experience of Online Shopping for Blind or Low Vision Users (2021) · cites this paper as [41]
Cites
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297 · see citing works
Extracting and Ranking Travel Tips from User-Generated Reviews
Guy, I., Mejer, A., Nus, A., & Raiber, F. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052632 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Big data analytics in smart grids: a review
Zhang, Y., Huang, T., & Bompard, E. F. (2018) · Energy Informatics · doi:10.1186/s42162-018-0007-5 · Cited by 404 · see citing works
Liu, Y., Jiang, C., & Zhao, H.-M. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113079 · Cited by 119 · see citing works
Xu, X. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113467 · Cited by 78 · see citing works
Antisocial online behavior detection using deep learning
Zinovyeva, E., Härdle, W. K., & Lessmann, S. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113362 · Cited by 68 · see citing works
Generating Product Descriptions from User Reviews
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313532 · Cited by 41 · see citing works
“Another related body of research has focused on extracting experiences [50, 53] and tips [21, 72] from user reviews.”
BackgroundWang, R., Chen, Z., Zhang, M. R., Li, Z., Liu, Z., Dang, Z., Yu, C., & Chen, X. '. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445547 · Cited by 42 · see citing works
“Prior work towards general users investigated extracting experiences [21, 24], tips [11, 41] or snippets suitable for product descriptions [7, 25], yet it remains”
BackgroundExtracting API Tips from Developer Question and Answer Websites
Wang, S., Phan, N., Wang, Y., & Zhao, Y. (2019) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2019.00058 · Cited by 29 · see citing works
“Zhu et al. [64] present an unsupervised method to mine tips from customer reviews.”
MethodSohrabi, M., & Hemmatian, F. (2019) · Multimedia tools and applications · doi:10.1007/s11042-019-7586-4 · Cited by 29 · see citing works
Will the relevance of review language and destination attractions be helpful? A data-driven approach
Hlee, S., Lee, H., Koo, C., & Chung, N. (2020) · Journal Of Vacation Marketing · doi:10.1177/1356766720950356 · Cited by 26 · see citing works
“…(Hlee et al., 2016; Liu and Park, 2015; Racherla and Friske, 2012; Yang et al., 2016), rating of product (Huang et al., 2015; Yang et al., 2017; Zhu et al., 2018), number of images (Hlee et al., 2019; Yang et al., 2017), number of friends as an indicator of reviewer reputation (Hlee et al.,…”
BackgroundThe impact of word sense disambiguation on stock price prediction
Hogenboom, A., Brojba-Micu, A., & Frasincar, F. (2021) · Expert systems with applications · doi:10.1016/j.eswa.2021.115568 · Cited by 18
Lin, J., Jiang, X., Li, Q., & Wang, C. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110764 · Cited by 16 · see citing works
Generating Tips from Product Reviews
Hirsch, S., Novgorodov, S., Guy, I., & Nus, A. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441755 · Cited by 15 · see citing works
“Tip extraction methods have been previously studied in other domains, especially travel [15, 37, 43].”
MethodJiang, C.-X., Zhu, J.-L., & Xu, Q.-F. (2020) · Electronic Commerce Research · doi:10.1007/s10660-020-09418-z · Cited by 13
“For example, [47] propose a unified unsupervised framework to address the issue of opinion spamming from the review deviation perspective.”
BackgroundAn Improved Feature Selection Based on Naive Bayes with Kernel Density Estimator for Opinion Mining
Sethuraman, R., & Athisayam, J. S. K. (2021) · The Arabian journal for science and engineering · doi:10.1007/s13369-021-05381-5 · Cited by 11
Query-aware Tip Generation for Vertical Search
Yang, Y., Hao, J., Li, C., Wang, Z.-L., Wang, J., Zhang, F.-Z., Rao, F., Hou, P.-X., Zhang, G., & Wang, Z.-Y. (2020) · International Conference on Information and Knowledge Management · doi:10.1145/3340531.3412740 · Cited by 11 · see citing works
“Based on user reviews, earlier work generate tips for POIs with natural language generation techniques, such as unsupervised extractive [32, 37] and abstractive methods [11, 12].”
MethodAn Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content
Luo, H., Song, W., Zhou, W., Lin, X.-D., & Yu, S. (2023) · Sustainability · doi:10.3390/su151813336 · Cited by 10 · see citing works
Pourramezani, K., Baghaee, H. R., & Gharehpetian, G. B. (2025) · Energy Reports · doi:10.1016/j.egyr.2025.08.049 · Cited by 12 · see citing works
Song, M. (2021) · Asian Journal of Beauty and Cosmetology · doi:10.20402/ajbc.2021.0169 · Cited by 9
“분석 방법 소비자가 남긴 빅데이터를 분석하는 연구가 활발하게 수행되면서 다양한 학문 연구 분야에서 빅데이터 분석 방법인 텍스트 마이닝 기 법을 활용한 연구가 활발하게 수행되고 있다(Zhu et al., 2018; Kim & Kim, 2016).”
Deep or simple models for semantic tagging?
Li, J., Li, Y., Wang, X., & Tan, W.-C. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3407790.3407844 · Cited by 6 · see citing works
“[60] observed that a practical tip tends to mention the speciality of a service, i.”
BackgroundDescriptions from the Customers
Novgorodov, S., Guy, I., Elad, G., & Radinsky, K. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3418202 · Cited by 12 · see citing works
“Another related body of research has focused on extracting experiences [54, 58] and tips [23, 80] from user reviews.”
BackgroundTipScreener: A Framework for Mining Tips for Online Review Readers
Luo, H., Song, W., & Zhou, W. (2022) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer17040087 · Cited by 3 · see citing works
Analyzing the Support Level for Tips Extracted from Product Reviews
Farber, M., Carmel, D., Kuchy, L., & Mejer, A. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531805 · Cited by 1 · see citing works
“There have been a long line of research on tips extraction, in domains such TripAdvisor [8, 27], restaurants [7, 27], hotels [12], and even from a community question answering site [25].”
BackgroundSemantic model to extract tips from hotel reviews
Kumar, S. H., & Chowdary, C. R. (2020) · Electronic Commerce Research · doi:10.1007/s10660-020-09446-9 · Cited by 3 · see citing works
Wahid, D. F., & Hassini, E. (2023) · Data & Knowledge Engineering · doi:10.1016/j.datak.2023.102238 · Cited by 2 · see citing works
Song, M., Cho, Y., & Yim, M.-J. (2022) · Asian Journal of Beauty and Cosmetology · doi:10.20402/ajbc.2022.0072 · Cited by 2
“화장품 구매 선택 속성은 화장품 구매 태도에 영향을 미치며(Seo & Kim, 2009), 다른 속성과 어떻게 차별화되는가를 알 수 있도록 하 고(Day, 1980), 선택을 통해 사용하고 난 후의 지각되는 만족도를 의 미(Ko & Seo, 2009) 있다(Zhu et al., 2018; Kim & Kim, 2016).”
The Tip of the Buyer: Extracting Product Tips from Reviews
Hirsch, S., Novgorodov, S., Guy, I., & Nus, A. (2022) · ACM Trans. Internet Techn. · doi:10.1145/3547140 · Cited by 2 · see citing works
“Closest to our work is the research by Guy et al. [23] and by Zhu et. al [69].”
MethodEvaluating Online Products Using Text Mining: A Reliable Evidence-Based Approach
Xu, H., Wei, R., Degroof, R., & Carberry, J. (2022) · International Journal of Semantic Computing · doi:10.1142/s1793351x22500064 · Cited by 2
“Zhu et al. proposed TipSelector, an unsupervised algorithm that provides high quality hints without annotated training data [25].”
BackgroundLearning to Generate Tips from Song Reviews
Zang, J., Gao, C., Chen, Y., Xu, R., Zhou, L., & Wang, X. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.049 · Cited by 2 · see citing works
Generating Tips from Song Reviews: A New Dataset and Framework
Zang, J., Gao, C., Chen, Y., Xu, R., Zhou, L.-J., & Wang, X. (2022) · arXiv.org · doi:10.48550/arxiv.2205.06985 · Cited by 1
“Although tip generation has been studied in other domains such as product [6] and travel [5, 33], tips of music songs are different in the following two aspects: 1) they are more diverse in patterns since the characteristics, context, or affection of the song may be perceived by users variously; 2)…”
BackgroundStylistic Pattern Guided Tip Extraction from Music Reviews
Zang, J., & Dong, A. (2022) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis55630.2022.00077
“Most previous studies focus on tip generation for other domains, such as studies of templates for the travel domain [5, 6] and abstractive methods in the product domain [7].”
BackgroundQuantifying Customer Review by Integrating Multiple Source of Knowledge
Liu, Y., Li, X., & Wang, M. (2019) · International Conference on Machine Learning and Computing · doi:10.1145/3318299.3318309
Selecting Comparative Sets of Reviews Across Multiple Items
Le, T.-H., & Lauw, H. W. (2025) · International Conference on Extending Database Technology · doi:10.48786/edbt.2025.02
- Authors
- Zhu, D., Lappas, T., & Zhang, J.
- Affiliations
- Stevens Institute of Technology (US)
- University of Massachusetts Lowell (US)
- Corresponding author
- Lappas, Theodoros
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 107
- Pages
- 116-124
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2790436581
- Cited by
- 32
- References
- 0
- Citations by year
- 2025: 1 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 9 · 2019: 5 · 2018: 1 · 2012: 1
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- customer reviews, unsupervised learning, hospitality industry
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