{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T07:40:22Z","timestamp":1782286822297,"version":"3.54.5"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T00:00:00Z","timestamp":1678060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s00500-023-07897-4","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T13:03:41Z","timestamp":1678107821000},"page":"6281-6296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Deep Hybrid Model for fake review detection by jointly leveraging review text, overall ratings, and aspect ratings"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9195-4920","authenticated-orcid":false,"given":"Ramadhani Ally","family":"Duma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-7572","authenticated-orcid":false,"given":"Zhendong","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5210-259X","authenticated-orcid":false,"given":"Ally S.","family":"Nyamawe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6797-8148","authenticated-orcid":false,"given":"Jude","family":"Tchaye-Kondi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3137-2398","authenticated-orcid":false,"given":"Abdulganiyu Abdu","family":"Yusuf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,6]]},"reference":[{"issue":"1","key":"7897_CR1","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1080\/10864415.2016.1061471","volume":"20","author":"SN Ahmad","year":"2015","unstructured":"Ahmad SN, Laroche M (2015) How do expressed emotions affect the helpfulness of a product review? evidence from reviews using latent semantic analysis. Int J Electron Commer 20(1):76\u2013111","journal-title":"Int J Electron Commer"},{"key":"7897_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-022-06806-5","volume":"26","author":"N Alsharif","year":"2022","unstructured":"Alsharif N (2022) Fake opinion detection in an e-commerce business based on a long-short memory algorithm. Soft Comput 26:1\u20138","journal-title":"Soft Comput"},{"issue":"5","key":"7897_CR3","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.1007\/s00500-019-04107-y","volume":"24","author":"MZ Asghar","year":"2020","unstructured":"Asghar MZ, Ullah A, Ahmad S, Khan A (2020) Opinion spam detection framework using hybrid classification scheme. Soft Comput 24(5):3475\u20133498","journal-title":"Soft Comput"},{"issue":"4","key":"7897_CR4","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.ipm.2019.03.002","volume":"56","author":"R Barbado","year":"2019","unstructured":"Barbado R, Araque O, Iglesias CA (2019) A framework for fake review detection in online consumer electronics retailers. Inf Process Manag 56(4):1234\u20131244","journal-title":"Inf Process Manag"},{"issue":"22","key":"7897_CR5","doi-asserted-by":"publisher","first-page":"20213","DOI":"10.1007\/s00521-022-07531-8","volume":"34","author":"G Bathla","year":"2022","unstructured":"Bathla G, Singh P, Singh RK, Cambria E, Tiwari R (2022) Intelligent fake reviews detection based on aspect extraction and analysis using deep learning. Neural Comput Appl 34(22):20213\u201320229","journal-title":"Neural Comput Appl"},{"issue":"12","key":"7897_CR6","doi-asserted-by":"publisher","first-page":"18107","DOI":"10.1007\/s11042-021-10602-y","volume":"80","author":"P Bhuvaneshwari","year":"2021","unstructured":"Bhuvaneshwari P, Rao AN, Robinson YH (2021) Spam review detection using self attention based cnn and bi-directional lstm. Multimed Tools Appl 80(12):18107\u201318124","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"7897_CR7","doi-asserted-by":"publisher","first-page":"13079","DOI":"10.1007\/s11042-020-10299-5","volume":"80","author":"GS Budhi","year":"2021","unstructured":"Budhi GS, Chiong R, Wang Z (2021) Resampling imbalanced data to detect fake reviews using machine learning classifiers and textual-based features. Multimed Tools Appl 80(9):13079\u201313097","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"7897_CR8","doi-asserted-by":"publisher","first-page":"e0182487","DOI":"10.1371\/journal.pone.0182487","volume":"12","author":"W Chen","year":"2017","unstructured":"Chen W, Yeo CK, Lau CT, Lee BS (2017) A study on real-time low-quality content detection on twitter from the users\u2019 perspective. PLoS One 12(8):e0182487","journal-title":"PLoS One"},{"key":"7897_CR9","doi-asserted-by":"crossref","unstructured":"Cheng Z, Ding Y, Zhu L, Kankanhalli M (2018) Aspect-aware latent factor model: rating prediction with ratings and reviews. In: Proceedings of the 2018 world wide web conference, pp 639\u2013648","DOI":"10.1145\/3178876.3186145"},{"key":"7897_CR10","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.eswa.2018.07.005","volume":"114","author":"L-Y Dong","year":"2018","unstructured":"Dong L-Y, Ji S-J, Zhang C-J, Zhang Q, Chiu DW, Qiu L-Q, Li D (2018) An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews. Expert Syst Appl 114:210\u2013223","journal-title":"Expert Syst Appl"},{"key":"7897_CR11","unstructured":"Ellson A (2018) A third of tripadvisor reviews are fake as cheats buy five stars. the times. https:\/\/www.thetimes.co.uk\/article\/hotel-and-caf-cheats-are-caught-trying-to-buy-tripadvisor-stars-027fbcwc8. Accessed: 2021-12-20"},{"key":"7897_CR12","doi-asserted-by":"crossref","unstructured":"Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Exploiting burstiness in reviews for review spammer detection. In: Proceedings of the international AAAI conference on web and social media 7:175\u2013184","DOI":"10.1609\/icwsm.v7i1.14400"},{"key":"7897_CR13","unstructured":"Feng S, Banerjee R, Choi Y (2012) Syntactic stylometry for deception detection. InL Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp 171\u2013175"},{"key":"7897_CR14","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.tourman.2016.09.009","volume":"59","author":"Y Guo","year":"2017","unstructured":"Guo Y, Barnes SJ, Jia Q (2017) Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tour Manage 59:467\u2013483","journal-title":"Tour Manage"},{"issue":"23","key":"7897_CR15","doi-asserted-by":"publisher","first-page":"17259","DOI":"10.1007\/s00521-020-04757-2","volume":"32","author":"P Hajek","year":"2020","unstructured":"Hajek P, Barushka A, Munk M (2020) Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput Appl 32(23):17259\u201317274","journal-title":"Neural Comput Appl"},{"key":"7897_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.121532","author":"P Hajek","year":"2022","unstructured":"Hajek P, Sahut J-M et al (2022) Mining behavioural and sentiment-dependent linguistic patterns from restaurant reviews for fake review detection. Technol Forecast Soc Change. https:\/\/doi.org\/10.1016\/j.techfore.2022.121532","journal-title":"Technol Forecast Soc Change"},{"issue":"6","key":"7897_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01385-6","volume":"3","author":"U Hayat","year":"2022","unstructured":"Hayat U, Saeed A, Vardag MHK, Ullah MF, Iqbal N (2022) Roman urdu fake reviews detection using stacked lstm architecture. SN Comput Sci 3(6):1\u20139","journal-title":"SN Comput Sci"},{"key":"7897_CR18","first-page":"755","volume":"4","author":"M Hu","year":"2004","unstructured":"Hu M, Liu B (2004) Mining opinion features in customer reviews. In AAAI 4:755\u2013760","journal-title":"In AAAI"},{"issue":"1","key":"7897_CR19","doi-asserted-by":"publisher","first-page":"e6539","DOI":"10.1002\/cpe.6539","volume":"34","author":"MS Jacob","year":"2022","unstructured":"Jacob MS, Selvi Rajendran P (2022) Fuzzy artificial bee colony-based cnn-lstm and semantic feature for fake product review classification. Concurr Comput Pract Exp 34(1):e6539","journal-title":"Concurr Comput Pract Exp"},{"key":"7897_CR20","doi-asserted-by":"crossref","unstructured":"Jindal N, Liu B (2007) Review spam detection. In: Proceedings of the 16th international conference on World Wide Web, pp 1189\u20131190","DOI":"10.1145\/1242572.1242759"},{"key":"7897_CR21","doi-asserted-by":"crossref","unstructured":"Jindal N, Liu B, Lim E-P (2010) Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp 1549\u20131552","DOI":"10.1145\/1871437.1871669"},{"issue":"8","key":"7897_CR22","doi-asserted-by":"publisher","first-page":"11765","DOI":"10.1007\/s11042-020-10183-2","volume":"80","author":"RK Kaliyar","year":"2021","unstructured":"Kaliyar RK, Goswami A, Narang P (2021) Fakebert: fake news detection in social media with a bert-based deep learning approach. Multimed Tools Appl 80(8):11765\u201311788","journal-title":"Multimed Tools Appl"},{"key":"7897_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.104066","volume":"97","author":"ZY Khan","year":"2021","unstructured":"Khan ZY, Niu Z, Nyamawe AS, Ul Haq I (2021) A deep hybrid model for recommendation by jointly leveraging ratings, reviews and metadata information. Eng Appl Artif Intell 97:104066","journal-title":"Eng Appl Artif Intell"},{"issue":"4","key":"7897_CR24","first-page":"3523","volume":"6","author":"S Kokate","year":"2015","unstructured":"Kokate S, Tidke B (2015) Fake review and brand spam detection using j48 classifier. IJCSIT Int J Comput Sci Inf Technol 6(4):3523\u20133526","journal-title":"IJCSIT Int J Comput Sci Inf Technol"},{"key":"7897_CR25","doi-asserted-by":"crossref","unstructured":"Li H, Fei G, Wang S, Liu B, Shao W, Mukherjee A, Shao J (2017) Bimodal distribution and co-bursting in review spam detection. In: Proceedings of the 26th international conference on world wide web, pp 1063\u20131072","DOI":"10.1145\/3038912.3052582"},{"key":"7897_CR26","doi-asserted-by":"crossref","unstructured":"Li J, Fu Y, Liu D, Xu R (2020a). Improving fake product detection with aspect-based sentiment analysis. In: International conference on cognitive computing, pp 39\u201349. Springer","DOI":"10.1007\/978-3-030-59585-2_4"},{"key":"7897_CR27","doi-asserted-by":"publisher","first-page":"114585","DOI":"10.1016\/j.eswa.2021.114585","volume":"171","author":"J Li","year":"2021","unstructured":"Li J, Lv P, Xiao W, Yang L, Zhang P (2021) Exploring groups of opinion spam using sentiment analysis guided by nominated topics. Expert Syst Appl 171:114585","journal-title":"Expert Syst Appl"},{"issue":"11","key":"7897_CR28","doi-asserted-by":"publisher","first-page":"3554","DOI":"10.1007\/s10489-020-01764-7","volume":"50","author":"J Li","year":"2020","unstructured":"Li J, Wang X, Yang L, Zhang P, Yang D (2020) Identifying ground truth in opinion spam: an empirical survey based on review psychology. Appl Intell 50(11):3554\u20133569","journal-title":"Appl Intell"},{"key":"7897_CR29","doi-asserted-by":"crossref","unstructured":"Lim E-P, Nguyen V-A, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp 939\u2013948","DOI":"10.1145\/1871437.1871557"},{"key":"7897_CR30","doi-asserted-by":"publisher","first-page":"101865","DOI":"10.1016\/j.is.2021.101865","volume":"103","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang L, Shi T, Li J (2022) Detection of spam reviews through a hierarchical attention architecture with n-gram CNN and bi-LSTM. Inf Syst 103:101865","journal-title":"Inf Syst"},{"key":"7897_CR31","doi-asserted-by":"publisher","first-page":"101865","DOI":"10.1016\/j.is.2021.101865","volume":"103","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang L, Shi T, Li J (2022) Detection of spam reviews through a hierarchical attention architecture with n-gram cnn and bi-lstm. Inf Syst 103:101865","journal-title":"Inf Syst"},{"key":"7897_CR32","unstructured":"Luca M (2016) Reviews, reputation, and revenue: The case of yelp. com. Com (March 15, 2016). Harvard Business School NOM Unit Working Paper, (12-016)"},{"key":"7897_CR33","doi-asserted-by":"crossref","unstructured":"Luo N, Deng H, Zhao L, Liu Y, Wang X, Tan Z (2017) Multi-aspect feature based neural network model in detecting fake reviews. In: 2017 4th international conference on information science and control engineering (ICISCE), pp 475\u2013479. IEEE","DOI":"10.1109\/ICISCE.2017.106"},{"key":"7897_CR34","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.ijhm.2019.02.008","volume":"80","author":"Y Luo","year":"2019","unstructured":"Luo Y, Tang RL (2019) Understanding hidden dimensions in textual reviews on airbnb: an application of modified latent aspect rating analysis (lara). Int J Hosp Manag 80:144\u2013154","journal-title":"Int J Hosp Manag"},{"key":"7897_CR35","first-page":"1","volume":"35","author":"B Manaskasemsak","year":"2021","unstructured":"Manaskasemsak B, Tantisuwankul J, Rungsawang A (2021) Fake review and reviewer detection through behavioral graph partitioning integrating deep neural network. Neural Comput Appl 35:1\u201314","journal-title":"Neural Comput Appl"},{"key":"7897_CR36","first-page":"234","volume":"2","author":"SM Mohammad","year":"2013","unstructured":"Mohammad SM, Turney PD (2013) Nrc emotion lexicon. Nat Res Counc Canada 2:234","journal-title":"Nat Res Counc Canada"},{"key":"7897_CR37","doi-asserted-by":"publisher","first-page":"114318","DOI":"10.1016\/j.eswa.2020.114318","volume":"169","author":"R Mohawesh","year":"2021","unstructured":"Mohawesh R, Tran S, Ollington R, Xu S (2021) Analysis of concept drift in fake reviews detection. Expert Syst Appl 169:114318","journal-title":"Expert Syst Appl"},{"key":"7897_CR38","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R (2013a) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 632\u2013640","DOI":"10.1145\/2487575.2487580"},{"key":"7897_CR39","unstructured":"Mukherjee A, Liu B (2012) Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 339\u2013348"},{"key":"7897_CR40","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on World Wide Web, pp 191\u2013200","DOI":"10.1145\/2187836.2187863"},{"key":"7897_CR41","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Venkataraman V, Liu B, Glance N (2013b) What yelp fake review filter might be doing? In: Proceedings of the International AAAI Conference on Web and Social Media, (volume\u00a07)","DOI":"10.1609\/icwsm.v7i1.14389"},{"key":"7897_CR42","unstructured":"Noekhah S, Fouladfar E, Salim N, Ghorashi SH, Hozhabri AA (2014) A novel approach for opinion spam detection in e-commerce. In: Proceedings of the 8th IEEE international conference on E-commerce with focus on E-trust"},{"key":"7897_CR43","doi-asserted-by":"crossref","unstructured":"Ochi M, Okabe M, Onai R (2011) Rating prediction using feature words extracted from customer reviews. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp 1205\u20131206","DOI":"10.1145\/2009916.2010121"},{"key":"7897_CR44","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint arXiv:cs\/0506075","DOI":"10.3115\/1219840.1219855"},{"key":"7897_CR45","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.compeleceng.2018.02.015","volume":"67","author":"S Rajamohana","year":"2018","unstructured":"Rajamohana S, Umamaheswari K (2018) Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Comput Electr Eng 67:497\u2013508","journal-title":"Comput Electr Eng"},{"key":"7897_CR46","doi-asserted-by":"crossref","unstructured":"Rayana S, Akoglu L (2015) Collective opinion spam detection: Bridging review networks and metadata. In: Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining, pp 985\u2013994","DOI":"10.1145\/2783258.2783370"},{"issue":"18","key":"7897_CR47","doi-asserted-by":"publisher","first-page":"26597","DOI":"10.1007\/s11042-019-07788-7","volume":"78","author":"AU Rehman","year":"2019","unstructured":"Rehman AU, Malik AK, Raza B, Ali W (2019) A hybrid cnn-lstm model for improving accuracy of movie reviews sentiment analysis. Multimed Tools Appl 78(18):26597\u201326613","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"7897_CR48","first-page":"449","volume":"69","author":"J Ren","year":"2018","unstructured":"Ren J, Yeoh W, Shan Ee M, Popovi\u010d A (2018) Online consumer reviews and sales: examining the chicken-egg relationships. J Am Soc Inf Sci 69(3):449\u2013460","journal-title":"J Am Soc Inf Sci"},{"key":"7897_CR49","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ins.2017.01.015","volume":"385","author":"Y Ren","year":"2017","unstructured":"Ren Y, Ji D (2017) Neural networks for deceptive opinion spam detection: an empirical study. Inf Sci 385:213\u2013224","journal-title":"Inf Sci"},{"issue":"3","key":"7897_CR50","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1007\/s11042-016-3819-y","volume":"76","author":"JK Rout","year":"2017","unstructured":"Rout JK, Singh S, Jena SK, Bakshi S (2017) Deceptive review detection using labeled and unlabeled data. Multimed Tools Appl 76(3):3187\u20133211","journal-title":"Multimed Tools Appl"},{"issue":"22","key":"7897_CR51","doi-asserted-by":"publisher","first-page":"8650","DOI":"10.1016\/j.eswa.2015.07.019","volume":"42","author":"D Savage","year":"2015","unstructured":"Savage D, Zhang X, Yu X, Chou P, Wang Q (2015) Detection of opinion spam based on anomalous rating deviation. Expert Syst Appl 42(22):8650\u20138657","journal-title":"Expert Syst Appl"},{"key":"7897_CR52","doi-asserted-by":"publisher","first-page":"113513","DOI":"10.1016\/j.dss.2021.113513","volume":"144","author":"G Shan","year":"2021","unstructured":"Shan G, Zhou L, Zhang D (2021) From conflicts and confusion to doubts: examining review inconsistency for fake review detection. Decis Support Syst 144:113513","journal-title":"Decis Support Syst"},{"key":"7897_CR53","doi-asserted-by":"crossref","unstructured":"Sundermeyer M, Schl\u00fcter R, Ney H (2012) Lstm neural networks for language modeling. In: 13th annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2012-65"},{"key":"7897_CR54","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.ins.2020.03.063","volume":"526","author":"X Tang","year":"2020","unstructured":"Tang X, Qian T, You Z (2020) Generating behavior features for cold-start spam review detection with adversarial learning. Inf Sci 526:274\u2013288","journal-title":"Inf Sci"},{"key":"7897_CR55","doi-asserted-by":"crossref","unstructured":"Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. In: Proceeding of the 17th international conference on World Wide Web - WWW \u201908. ACM Press","DOI":"10.1145\/1367497.1367513"},{"issue":"2","key":"7897_CR56","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s10462-019-09697-5","volume":"53","author":"DU Vidanagama","year":"2020","unstructured":"Vidanagama DU, Silva TP, Karunananda AS (2020) Deceptive consumer review detection: a survey. Artif Intell Rev 53(2):1323\u20131352","journal-title":"Artif Intell Rev"},{"key":"7897_CR57","doi-asserted-by":"crossref","unstructured":"Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 783\u2013792","DOI":"10.1145\/1835804.1835903"},{"key":"7897_CR58","doi-asserted-by":"crossref","unstructured":"Wang H, Lu Y, Zhai C (2011) Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 618\u2013626","DOI":"10.1145\/2020408.2020505"},{"key":"7897_CR59","doi-asserted-by":"crossref","unstructured":"Wang X, Liu K, Zhao J (2017) Handling cold-start problem in review spam detection by jointly embedding texts and behaviors. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 366\u2013376","DOI":"10.18653\/v1\/P17-1034"},{"issue":"7","key":"7897_CR60","doi-asserted-by":"publisher","first-page":"3338","DOI":"10.3390\/app12073338","volume":"12","author":"C-H Weng","year":"2022","unstructured":"Weng C-H, Lin K-C, Ying J-C (2022) Detection of chinese deceptive reviews based on pre-trained language model. Appl Sci 12(7):3338","journal-title":"Appl Sci"},{"key":"7897_CR61","doi-asserted-by":"crossref","unstructured":"Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via time series pattern discovery. In: Proceedings of the 21st International Conference on World Wide Web, pp 635\u2013636","DOI":"10.1145\/2187980.2188164"},{"key":"7897_CR62","unstructured":"Xu Q, Zhao H (2012) Using deep linguistic features for finding deceptive opinion spam. In: Proceedings of COLING 2012: Posters, pp 1341\u20131350"},{"key":"7897_CR63","doi-asserted-by":"publisher","first-page":"16914","DOI":"10.1109\/ACCESS.2021.3051174","volume":"9","author":"J Yao","year":"2021","unstructured":"Yao J, Zheng Y, Jiang H (2021) An ensemble model for fake online review detection based on data resampling, feature pruning, and parameter optimization. IEEE Access 9:16914\u201316927","journal-title":"IEEE Access"},{"key":"7897_CR64","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.future.2019.07.044","volume":"102","author":"L You","year":"2020","unstructured":"You L, Peng Q, Xiong Z, He D, Qiu M, Zhang X (2020) Integrating aspect analysis and local outlier factor for intelligent review spam detection. Futur Gener Comput Syst 102:163\u2013172","journal-title":"Futur Gener Comput Syst"},{"key":"7897_CR65","unstructured":"You Z, Qian T, Liu B (2018) An attribute enhanced domain adaptive model for cold-start spam review detection. In: Proceedings of the 27th international conference on computational linguistics, pp 1884\u20131895"},{"key":"7897_CR66","unstructured":"Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners\u2019 guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820"},{"key":"7897_CR67","doi-asserted-by":"crossref","unstructured":"Zhu J, Zhu M, Wang H, Tsou BK (2009) Aspect-based sentence segmentation for sentiment summarization. In: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion, pp 65\u201372","DOI":"10.1145\/1651461.1651474"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-07897-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-07897-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-07897-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T04:20:13Z","timestamp":1682050813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-07897-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,6]]},"references-count":67,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["7897"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-07897-4","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,6]]},"assertion":[{"value":"1 February 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No formal consent is required for this kind of study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"For this kind of study, there is nothing that requires ethical approval.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}