Abstract
With the phenomenal growth of e-commerce, online review systems have become the normative dissemination mode of electronic word-of-mouth (eWOM). Unlike traditional WOM, consumers experience information overload in eWOM, thus they often read only a few reviews before making their purchase decision. Consumers tend to search for the most helpful and useful reviews from the large volume of posted reviews. To identify the most relevant reviews, this study applied both non-context features that affect the helpfulness of reviews and the context information that the review texts imply. The test performance and the results of the proposed method more effectively extracted reviews that provided the helpful information to consumers than the ordinary voting-based top-review list.
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Alsmadi A, AlZu’bi S, Hawashin B, Al-Ayyoub M, Jararweh Y (2020) Employing deep learning methods for predicting helpful reviews. Inf Commun Syst 11:7–12
Bae YK (2022) coupang marketplace, recruiting ‘seller ambassadors’ to expand communication between sellers. Maeil Business, Seoul
Biau G (2012) Analysis of a random forests model. J Mach Learn Res 13:1063–1095
Bloomberg (2021a) Coupang files for IPO as South Korea heads into boom year. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=148698930&site=eds-live&scope=site
Bloomberg (2021b) SoftBank-backed Coupang gets debut gain in top 2021b U.S. IPO. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=149217274&site=eds-live&scope=site
Brightlocal (2018) Local Consumer Review Survey. https://brightlocal.com/research/local-consumer-review-survey
Cao Q, Duan W, Gan Q (2011) Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach. Decis Support Syst 50:511–521. https://doi.org/10.1016/j.dss.2010.11.009
Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning pp 161–168. https://doi.org/10.1145/1143844.1143865
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1613/jair.953
Chen Y, Xie J (2008) Online consumer review: word-of-mouth as a new element of marketing communication mix. Manag Sci 54:477–491. https://doi.org/10.1287/mnsc.1070.0810
Choi SB, Kim JM (2018) A comparative analysis of electronic service quality in the online open market and social commerce: the case of Korean young adults. Serv Bus 12:403–433. https://doi.org/10.1007/s11628-017-0352-7
Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. ACM Int Conf Proc Ser 148:233–240. https://doi.org/10.1145/1143844.1143874
Day MY, Lin YD (2017) Deep learning for sentiment analysis on google play consumer review. In: 2017 IEEE int conference on information reuse and integration, pp 382–388. https://ieeexplore.ieee.org/document/8102961
Ghose A, Ipeirotis PG (2011) Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans Knowl Data Eng 23:1498–1512
Guan M, Cho S, Petro R, Zhang W, Pasche B, Topaloglu U (2019) Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes. JAMIA Open 2:139–149. https://doi.org/10.1093/jamiaopen/ooy061
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hong Y, Lu J, Yao J, Zhu Q, Zhou G (2012) What reviews are satisfactory: novel features for automatic helpfulness voting. In: 2012 Int ACM SIGIR Conference on Res and Development in Information Retrieval, pp 495–504. https://doi.org/10.1145/2348283.2348351
Hu N, Bose I, Gao Y, Liu L (2011) Manipulation in digital word-of-mouth: a reality check for book reviews. Decis Support Syst 50:627–635. https://doi.org/10.1016/j.dss.2010.08.013
Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data - recommendations for the use of performance metrics. 2013 Humaine Association Conference on ACII, pp 245–251. https://ieeexplore.ieee.org/document/6681438
Kim G, Chae BK, Olson DL (2013) A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models. Serv Bus 7:167–182. https://doi.org/10.1007/s11628-012-0147-9
Konlpy (2014) KoNLPy: Korean NLP in Python. https://konlpy.org/en/latest
Krishnamoorthy S (2015) Linguistic features for review helpfulness prediction. Expert Syst Appl 42:3751–3759. https://doi.org/10.1016/j.eswa.2014.12.044
Kwon BC, Kim SH, Duket T, Catalán A, Yi JS (2015) Do people really experience information overload while reading online reviews? Int J Hum–comput Interact 31:959–973. https://doi.org/10.1080/10447318.2015.1072785
Lee S, Choeh JY (2014) Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst Appl 41:3041–3046. https://doi.org/10.1016/j.eswa.2013.10.034
Lee S, Choeh JY (2018) The interactive impact of online word-of-mouth and review helpfulness on box office revenue. Manag Decis 56:849–866. https://doi.org/10.1108/MD-06-2017-0561
Lee PJ, Hu YH, Lu KT (2018) Assessing the helpfulness of online hotel reviews: a classification-based approach. Telemat Inform 35:436–445. https://doi.org/10.1016/j.tele.2018.01.001
Li M, Tan CH, Wei KK, Wang K (2017) Sequentiality of product review information provision: an information foraging perspective. Manag Inf Syst 41:386–892. https://doi.org/10.25300/MISQ/2017/41.3.09
Liu J, Cao Y, Lin C-Y, Huang Y, Zhou M (2007) Low-quality product review detection in opinion summarization. In: 2007 joint conference on EMNLP-CoNLL. pp 334–342. https://aclanthology.org/D07-1035
Liu Y, Huang X, An A, Yu X (2008) Modeling and predicting the helpfulness of online reviews. IEEE Int Conf Data Mining. https://doi.org/10.1109/ICDM.2008.94
Liu P, Qiu X, Xuanjing H (2016) Recurrent neural network for text classification with multi-task learning. In: 2016 int joint conference on artificial intelligence, pp 2873–2879. https://doi.org/10.48550/arXiv.1605.05101
Malik MSI, Hussain A (2017) Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput Hum Behav 73:290–302. https://doi.org/10.1016/j.chb.2017.03.053
Miguéis VL, Camanho AS, Borges J (2017) Predicting direct marketing response in banking: comparison of class imbalance methods. Serv Bus 11:831–849. https://doi.org/10.1007/s11628-016-0332-3
Mikolov T, Karafiát M, Burget L, Černocký J, Khudanpur S (2010) Recurrent neural network based language model. The 11th Annual Conference of the ISCA pp 1045–1048. https://doi.org/10.21437/Interspeech.2010-343
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. Int Conf Learn Represent. https://doi.org/10.48550/arXiv.1301.3781
Milano S, Taddeo M, Floridi L (2020) Recommender systems and their ethical challenges. AI Soc 35:957–967. https://doi.org/10.1007/s00146-020-00950-y
Nikolay A, Anindya G, Panagiotis GI (2011) Deriving the pricing power of product features by mining consumer reviews. Manag Sci 57:1485–1509. https://doi.org/10.1287/mnsc.1110.1370
Palalic R, Ramadani V, Mariam Gilani S, Gërguri-Rashiti S, Dana L-P (2020) Social media and consumer buying behavior decision: what entrepreneurs should know? Manag Decis. https://doi.org/10.1108/MD-10-2019-1461
Podium (2017) State of online review. http://learn.podium.com/rs/841-BRM-380/images/2017-SOOR-Infographic.jpg
Qu X, Li X, Rose JR (2018) Review helpfulness assessment based on convolutional neural network. CoRR. https://arxiv.org/abs/1808.09016
Raj H, Weihong Y, Banbhrani SK, Dino SP (2018) LSTM based short message service (SMS) modeling for spam classification. In: 2018 ACM Int conference proceeding series, pp 76–80. https://doi.org/10.1145/3231884.3231895
Rao G, Huang W, Feng Z, Cong Q (2018) LSTM with sentence representations for document-level sentiment classification. Neurocomputing 308:49–57. https://doi.org/10.1016/j.neucom.2018.04.045
Ray S (2019) A quick review of machine learning algorithms. In: International conference on machine learning, big data, cloud and parallel computing pp 35–39. https://doi.org/10.1109/comitcon.2019.8862451
Salehan M, Kim DJ (2016) Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis Support Syst 81:30–40. https://doi.org/10.1016/j.dss.2015.10.006
Saumya S, Singh JP, Baabdullah AM, Rana NP, Dwivedi YK (2018) Ranking online consumer reviews. Electr Commer Res Appl 29:78–89. https://doi.org/10.1016/j.elerap.2018.03.008
Saumya S, Singh JP, Dwivedi YK (2020) Predicting the helpfulness score of online reviews using convolutional neural network. Soft Comput 24:10989–11005. https://doi.org/10.1007/s00500-019-03851-54
Singh JP, Irani S, Rana NP, Dwivedi YK, Saumya S, Roy PK (2017) Predicting the “helpfulness” of online consumer reviews. J Bus Res 70:346–355. https://doi.org/10.1016/j.jbusres.2016.08.008
Srivastava V, Kalro AD (2019) Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. J Interact Market 48:33–50. https://doi.org/10.1016/j.jbusres.2016.08.008
Sun X, Han M, Feng J (2019) Helpfulness of online reviews: examining review informativeness and classification thresholds by search products and experience products. Decis Support Syst 124:113099. https://doi.org/10.1016/j.dss.2019.113099
Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A (2010) Sentiment in short strength detection informal text. J Am Soc Inf Sci Technol 61:2544–2558. https://doi.org/10.1002/asi.21416
Willemsen LM, Neijens PC, Bronner F, de Ridder JA (2011) Highly recommended!” The content characteristics and perceived usefulness of online consumer reviews. J Comput-Mediat Commun 17:19–38. https://doi.org/10.1111/j.1083-6101.2011.01551.x
Zhang Y, Zhang D (2014) Automatically predicting the helpfulness of online reviews. In: 2014 IEEE 15th int conference on information reuse and integration, pp 662–668. https://doi.org/10.1109/IRI.2014.7051953
Zhou S, Guo B (2017) The order effect on online review helpfulness: a social influence perspective. Decis Support Syst 93:77–87. https://doi.org/10.1145/1143844.1143865
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Lee, KK., Lee, HH., Cho, SJ. et al. The context-based review recommendation system in e-business platform. Serv Bus 16, 991–1013 (2022). https://doi.org/10.1007/s11628-022-00502-y
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DOI: https://doi.org/10.1007/s11628-022-00502-y