Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns | Data Mining and Knowledge Discovery Skip to main content

Advertisement

Springer Nature Link
Log in

Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

Customer reviews are an essential source of information to consumers. Meanwhile, opinion spams spread widely and the detection of spam reviews becomes critically important for ensuring the integrity of the echo system of online reviews. Singleton spam reviews—one-time reviews—have spread widely of late as spammers can create multiple accounts to purposefully cheat the system. Most available techniques fail to detect this cunning form of malicious reviews, mainly due to the scarcity of behaviour trails left behind by singleton spammers. Available approaches also require extensive feature engineering, expensive manual annotation and are less generalizable. Based on our thorough study of spam reviews, it was found that genuine opinions are usually directed uniformly towards important aspects of entities. In contrast, spammers attempt to counter the consensus towards these aspects while covering their malicious intent by adding more text but on less important aspects. Additionally, spammers usually target specific time periods along products’ lifespan to cause maximum bias to the public opinion. Based on these observations, we present an unsupervised singleton spam review detection model that runs in two steps. Unsupervised deep aspect-level sentiment model employing deep Boltzmann machines first learns fine-grained opinion representations from review texts. Then, an LSTM network is trained on opinion learned representation to track the evolution of opinions through the fluctuation of sentiments in a temporal context, followed by the application of a Robust Variational Autoencoder to identify spam instances. Experiments on three benchmark datasets widely used in the literature showed that our approach outperforms strong state-of-the-art baselines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://www.brightlocal.com/learn/local-consumer-review-survey/.

  2. https://github.com/yassienshaalan/DTOPS.

  3. https://keras.io/.

References

  • Ahmed M, Mahmood AN, Hu J (2016) A survey of network anomaly detection techniques. J Netw Comput Appl 60:19–31

    Article  Google Scholar 

  • Akcay S, Atapour-Abarghouei A, Breckon TP (2018) Ganomaly: semi-supervised anomaly detection via adversarial training. In: Proceedings of Asian conference on computer vision (ACCV), pp 622–637

  • Akoglu L, Chandy C R andFaloutsos (2013) Opinion fraud detection in online reviews by network effects. In: Proceedings of international conference on web and social media (ICWSM), pp 2–11

  • Aygun RC, Yavuz AG (2017) Network anomaly detection with stochastically improved autoencoder based models. In: Proceedings of international conference on cyber security and cloud computing (CSCloud), pp 193–198

  • Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of international conference on machine learning (ICML), pp 37–49

  • Blei D, Ng A, Jordan I (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  • Bontemps L, McDermott J, Le-Khac NA (2016) Collective anomaly detection based on long short-term memory recurrent neural networks. In: Proceedings of international conference on foundations of restoration ecology (ICFRE), pp 141–152

  • Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Byeon W, Breuel TM, Raue F, Liwicki M (2015) Scene labeling with lstm recurrent neural networks. In: Proceedings of conference on computer vision and pattern recognition (CVPR), pp 3547–3555

  • Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58:1–37

    Article  MathSciNet  Google Scholar 

  • Card D, Tan C, Smith N (2018) Neural models for documents with metadata. In: Proceedings of the Association for Computational Linguistics (ACL), pp 2031–2040

  • Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of conference on empirical methods in natural language processing (EMNLP), pp 452–461

  • Christy A, Gandhi GM, Vaithyasubramanian S (2015) Cluster based outlier detection algorithm for healthcare data. Procedia Comput Sci 50:209–215

    Article  Google Scholar 

  • Colhon M, Vlăduţescu S, Negrea X (2017) How objective a neutral word is? A neutrosophic approach for the objectivity degrees of neutral words. Symmetry 9(11):280

    Article  Google Scholar 

  • Devlin J, Ming-Wei C, Kenton L, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), pp 4171–4186

  • Erfani SM, Baktashmotlagh M, Moshtaghi M, Nguyen V, Leckie C, Bailey J, Ramamohanarao K (2017) From shared subspaces to shared landmarks: a robust multi-source classification approach. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI)

  • Eskin E, Arnold A, Prerau M, Portnoy L, Stolfo S (2002) A geometric framework for unsupervised anomaly detection. In: Barbará D, Jajodia S (eds) Applications of data mining in computer security. Springer, Boston, pp 77–101

    Chapter  Google Scholar 

  • Estiri H, Murphy SN (2019) Semi-supervised encoding for outlier detection in clinical observation data. Comput Methods Programs Biomed 181:104830

    Article  Google Scholar 

  • Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Exploiting burstiness in reviews for review spammer detection. In: Proceedings of international conference on web and social media (ICWSM), pp 175–184

  • Fu P, Lin Z, Yuan F, Wang W, Meng D (2018) Learning sentiment-specific word embedding via global sentiment representation. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI)

  • Ganu G, Elhadad N, Marian A (2009) Beyond the stars: improving rating predictions using review text content. Web Databases 9:1–6

    Google Scholar 

  • García-Pablos A, Cuadros M, Rigau G (2018) W2vlda: Almost unsupervised system for aspect based sentiment analysis. Expert Syst Appl 91:127–137

    Article  Google Scholar 

  • Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2016) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28:18–28

    Article  Google Scholar 

  • Ghanem B, Rosso P, Rangel F (2020) An emotional analysis of false information in social media and news articles. ACM Trans Internet Technol 20(2):1–18

    Article  Google Scholar 

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw 18(5):602–610

    Article  Google Scholar 

  • Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2016) Lstm: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    Article  MathSciNet  Google Scholar 

  • Gupta P, Chaudhary Y, Buettner F, Schutze H (2019) Document informed neural autoregressive topic models with distributional prior. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI), pp 6505–6512

  • Görnitz N, Kloft M, Rieck K, Brefeld U (2013) Toward supervised anomaly detection. J Artif Intell Res 46:235–262

    Article  MathSciNet  Google Scholar 

  • Hai Z, Chang K, Kim J (2011) Implicit feature identification via co-occurrence association rule mining. In: Proceedings of international conference on intelligent text processing and computational linguistics (CICLing), pp 393–404

  • He S, Wang S, Lan W, Fu H, Ji Q (2013) Facial expression recognition using deep Boltzmann machine from thermal infrared images. In: Proceedings of conference of the Association for the Advancement of Affective Computing (AAAC), pp 239–244

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Hu M, Liu B (2004a) Mining and summarizing customer reviews. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 168–177

  • Hu M, Liu B (2004b) Mining and summarizing customer reviews. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 168–177

  • Huayi L, Geli F, Shuai W, Bing L, Weixiang S, Mukherjee A, Jidong S (2017) Bimodal distribution and co-bursting in review spam detection. In: Proceedings of international world wide web conference (WWW), pp 1063–1072

  • Hundman K, Constantinou V, Laporte C, Colwell I, Soderstrom T (2018) Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 387–395

  • Jakob N, Gurevych I (2010) Extracting opinion targets in a single- and cross-domain setting with conditional random fields. In: Proceedings of empirical methods in natural language processing (EMNLP), pp 1035–1045

  • Jo Y, Oh A (2011) Aspect and sentiment unification model for online review analysis. In: Proceedings of conference on web search and data mining (WSDM), pp 815–824

  • Kazemi S, Abghari S, Lavesson N, Johnson H, Ryman P (2016) Open data for anomaly detection in maritime surveillance. Expert Syst Appl 40(14):5719–5729

    Article  Google Scholar 

  • Kim S, Choi Y, Lee M (2015) Deep learning with support vector data description. Neurocomputing 165:111–117

    Article  Google Scholar 

  • Kobayashi N, Iida R, Inui K, Matsumoto Y (2006) Opinion mining on the web by extracting subject-aspect-evaluation relations. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI), pp 86–91

  • Kou Y, Lu CT, Chen D (2006) Spatial weighted outlier detection. In: Proceedings of Society for Industrial and Applied Mathematics (SIAM), pp 614–618

  • Kumar D, Shaalan Y, Zhang X, Chan J (2018) Identifying singleton spammers via spammer group detection. In: Proceedings of Pacific-Asia conference on knowledge discovery and data mining (PAKDD), pp 175–184

  • Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. In: Proceedings of Association for the Advancement of Artificial Intelligence (AAAI), pp 2267–2273

  • Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of international conference on machine learning (ICML), pp 1188–1196

  • Le Q, Mikolov T (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci 62(12):3412–3427

    Article  Google Scholar 

  • Li J, Cardie C, Li S (2013) Topicspam: a topic-model-based approach for spam detection. In: Proceedings of The Association for Computational Linguistics (ACL), pp 217–221

  • Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the Association for Computational Linguistics (ACL), pp 1566–1576

  • Lim E, Nguyen V, Jindal N, Liu B, Lauw HW (2008) Detecting product review spammers using rating behaviors. In: Proceedings of conference on information and knowledge management (CIKM), pp 939–948

  • Liu B (2010) Sentiment analysis and subjectivity. Handbook of natural language orocessing, vol 2, 2nd edn. Chapman and Hall, Boca Raton, pp 627–666

    Google Scholar 

  • Liu B, Zhang L (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167

    Article  Google Scholar 

  • Liu F, Ting K, Zhou Z (2008) Isolation forest. In: Proceedings of international conference on data mining (ICDM), pp 413–422

  • Liu FT, Ting KM, Zhou ZH (2010) On detecting clustered anomalies using sciforest. In: Proceedings of European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD), pp 274–290

  • Logeswaran L, Lee H (2018) An efficient framework for learning sentence representations. In: Proceedings of international conference on learning representations (ICLR)

  • Luyang L, Qin B, Wenjing R, Liu T (2016) Document representation and feature combination for deceptive spam review detection. Neuro Comput 254:33–41

    Google Scholar 

  • Ma J, Sun L, Wang H, Zhang Y, Aickelin U (2016) Supervised anomaly detection in uncertain pseudo-periodic data streams. ACM Trans Internet Technol 16(1):235–262

    Article  Google Scholar 

  • Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. In: Proceedings of international joint conferences on artificial intelligence (IJCAI), pp 4068–4074

  • Maas A, Daly R, Pham P, Huang D, Ng A, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of The Association for Computational Linguistics (ACL)

  • Malhotra P, Lovekesh V, Shroff G, Argarwal P (2015) Long short term memory networks for anomaly detection in time series. In: Proceedings of European symposium on artificial neural networks (ESANN), pp 665–674

  • McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of conference on recommender systems (RecSys), pp 165–172

  • McAuley J, Leskovec J, Jurafsky D (2012) Learning attitudes and attributes from multiaspect reviews. In: Proceedings of international conference on data Mining (ICDM), pp 1020–1025

  • Mei Q, Ling X, Wondra M, Su H, Zhai C (2007) Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of international world wide web conference (WWW), pp 171–180

  • Miedema F (2018) Sentiment analysis with long short-term memory. Vrije Universiteit Amsterdam 1

  • Mihalcea R, Strapparava C (2009) The lie detector: explorations in the automatic recognition of deceptive language. In: Proceedings of international joint conference on natural language processing (IJCNLP), pp 309–312

  • Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119

    Google Scholar 

  • Moghaddam S, Ester M (2011) Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. In: Proceedings of conference on research and development in information retrieval (ACM SIGIR), pp 665–674

  • Mukherjee A, Liu B (2010) Modeling review comments. In: Proceedings of The Association for Computational Linguistics (ACL), pp 320–329)

  • Mukherjee A, Liu B, Wang J, Glance N, Jindal N (2011) Detecting group review spam. In: Proceedings of international world wide web conference (WWW), pp 93–94

  • Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R (2013) Spotting opinion spammers using behavioral footprints. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 632–640

  • Narisawa K, Hideo B, Hatano K, Takeda M (2007) Unsupervised spam detection based on string alienness measures. In: Proceedings of international conference on discovery science (DS), pp 161–172

  • Nguyen-Hoang B, Ha Q, Nghiem M (2016) Aspect-based sentiment analysis using word embedding restricted Boltzmann machines. In: Proceedings of international conference on computational social networks (CSoNet), pp 285–297

  • Ott M, Choi Y, Cardie C, Hancok J (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of The Association for Computational Linguistics (ACL), pp 309–319

  • Pennington J, Socher R, Manning C (2014) Glove: Global vectors forward representation. In: Proceedings of empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), pp 2227–2237

  • Phua C, Alahakoon D, Lee V (2004) Minority report in fraud detection: classification of skewed data. ACM SIGKDD Explor Newsl 6(1):50–59

    Article  Google Scholar 

  • Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Proceedings of international workshop on semantic evaluation-2014 task 4: aspect based sentiment analysis. In: Proceedings of international workshop on semantic evaluation (SemEval), pp 27–35

  • Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Manandhar S (2015) Proceedings of international workshop on semantic evaluation-2015 task 12: aspect based sentiment analysis. In: Proceedings of international workshop on semantic evaluation (SemEval), pp 486–495

  • Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, of The International Conference on Language Resources ODC, Evaluationq U (2016) Proceedings of international workshop on semantic evaluation-2016 task 5: aspect based sentiment analysis. In: Proceedings of international workshop on semantic evaluation (SemEval), pp 19–30

  • Principi E, Vesperini F, Squartini S, Piazza F (2017) Acoustic novelty detection with adversarial autoencoders. In: Proceedings of international joint conference on neural networks (IJCNN), pp 3324–3330

  • Qiu G, Liu B, Bu J, Chen C (2009) Expanding domain sentiment lexicon through double propagation. In: Proceedings of international joint conferences on artificial intelligence (IJCAI), pp 1199-1204

  • Racah E, Beckham C, Maharaj T, Kahou SE, Prabhat M, Pal C (2017) Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In: Proceedings of neural information processing systems (NIPS), pp 3402–3413

  • Rayana S, Akoglu L (2015) Collective opinion spam detection: Bridging review networks and metadata. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 985–994

  • Saini M, Sharan A (2017) Identifying deceptive opinion spam using aspect-based emotions and human behavior modeling. Int J Hybrid Inf Technol 10(1):447–456

    Google Scholar 

  • Sakurada M, Yairi T (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of workshop on machine learning for sensory data analysis (MLSDA)

  • Salakhutdinov R, Hinton G (2009) Deep Boltzmann machines. In: Proceedings of artificial intelligence and statistics (AISTATS), pp 448–455

  • Salvador S, Chan P, Brodie J (2004) Learning states and rules for time series anomaly detection. In: Proceedings of The Florida artificial intelligence research society (FLAIRS), pp 306–311

  • Sandulescu V, Ester M (2015) Detecting singleton review spammers using semantic similarity. In: Proceedings of international world wide web conference (WWW), pp 971–976

  • Sauper C, Barzilay R (2013) Automatic aggregation by joint modeling of aspects and values. J Artif Intell Res 46:89–127

    Article  MathSciNet  Google Scholar 

  • 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):8650–8657

    Article  Google Scholar 

  • Shehnepoor S, Salehi M, Farahbakhsh R, Crespi N (2019) Netspam: a network-based spam detection framework for reviews in online social media. IEEE Trans Inf Forensics Secur 12(7):1585–1595

    Article  Google Scholar 

  • Shojaee S, Murad M, A BA, Sharef NM, Nadali S (2013) Detecting deceptive reviews using lexical and syntactic features. In: Proceedings of international conference on intelligent systems design and applications (ISDA), pp 53–58

  • Siegel S, Castellan J (1988) Nonparametric statistics for the behavioral sciences, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  • Solberg HE, Lahti A (2005) Detection of outliers in reference distributions: performance of Horn’s algorithm. Clin Chem 51(12):2326–2332

    Article  Google Scholar 

  • Srivastava A, Kundu A, Sural S, Majumdar A (2008) Credit card fraud detection using hidden Markov model. IEEE Trans Dependable Secure Comput 5(1):37–48

    Article  Google Scholar 

  • Srivastava N, Salakhutdinov R, Hinton G (2013) Modeling documents with a deep Boltzmann machine. In: Proceedings of conference on uncertainty in artificial intelligence (UAI), pp 616–624

  • Sun J, Wang X, Xiong N, Shao J (2018) Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access 6:33353–33361

    Article  Google Scholar 

  • Sundermeyer M, Schlüter R, Ney H (2012) Lstm neural networks for language modeling. In: Proceedings of conference of the International Speech Communication Association (ISCA)

  • Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of neural information processing systems (NIPS), pp 3104–3112

  • Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2015) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the Association for Computational Linguistics (ACL), pp 1555–1565

  • Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: Liwc and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54

    Article  Google Scholar 

  • Titov I, McDonald R (2008) A joint model of text and aspect ratings for sentiment summarization. In: Proceedings of The Association for Computational Linguistics (ACL), pp 308–316

  • Titov I, McDonald R (2009) Modeling online reviews with multi-grain topic models. In: Proceedings of International World Wide Web Conference (WWW), pp 111–120

  • Toprak C, Jakob N, Gurevych I (2010) Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of The Association for Computational Linguistics (ACL), pp 575–584

  • van der Maaten L, Hinton G (2008) Visualizing high-dimensional data using t-sne. Mach Learn Res 9:2579–2605

    MATH  Google Scholar 

  • Verwimp L, Pelemans J, , Wambacq P (2017) Character-word lstm language models. In: Proceedings of conference of the European chapter of the Association for Computational Linguistics (EACL), pp 417–427

  • Wang L, Liu K, Cao Z, Zhao J, de Melo G (2015) Sentiment-aspect extraction based on restricted Boltzmann machines. In: Proceedings of the Association for Computational Linguistics (ACL), pp 616–625

  • Wang X, Liu K, Zhao J (2017) Detecting deceptive review spam via attention-based neural networks. In: Proceedings of national conference on Natural Language Processing and Chinese Computing (NLPCC), pp 866–876

  • Wang H, Bah MJ, Hammad M (2019) Progress in outlier detection techniques: a survey. IEEE Access 7:107964–108000

    Article  Google Scholar 

  • Xie S, Wang G, Lin S, Yu PS (2012) Review spam detection via time series pattern discovery. In: Proceedings of knowledge discovery and data mining (KDD), pp 823–831

  • Xie P, Deng Y, Xing E (2015) Diversifying restricted Boltzmann machine for document modeling. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 1315–1324

  • Xu H, Chen W, Zhao N, Li Z, Bu J, Li Z, Liu Y, Zhao Y, Pei D, Feng Y, Chen J (2018) Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In: Proceedings of International World Wide Web Conference (WWW), pp 187–196

  • Ye Y, Akoglu L (2017) Discovering opinion spammer groups by network footprints. In: Proceedings of joint European conference on machine learning and knowledge discovery in databases (ECML KDD), pp 97–113

  • Ye J, Kumar S, Akoglu L (2016) Temporal opinion spam detection by multivariate indicative signals. In: Proceedings of international conference on web and social media (ICWSM), pp 743–746

  • Zhang L, Liu B, Lim SH, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: Proceedings of international conference on computational linguistics (COLING), pp 1462–1470

  • Zhao W, Jiang J, Yan H, Li X (2010a) Jointly modeling aspects and opinions with a maxent-lda hybrid. In: Proceedings of empirical methods in natural language processing (EMNLP), pp 56–65

  • Zhao Y, Qin B, Hu S, Liu T (2010b) Generalizing syntactic structures for product attribute candidate extraction. In: Proceedings of conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), pp 377–380

  • Zhao S, Xu Z, Liu L, Guo M, Yun J (2018) Towards accurate deceptive opinion spam detection based on word order-preserving CNN. Math Probl Eng 2018:2410206

  • Zheng Y, Zhang H, Yu Y (2015) Detecting collective anomalies from multiple spatio-temporal datasets across different domains. In: Proceedings of international conference on advances in geographic information systems (ACM SIGSPATIAL), pp 1–10

  • Zhou C, Paffenroth R (2017) Anomaly detection with robust deep autoencoders. In: Proceedings of special interest group on knowledge discovery in data (ACM SIGKDD), pp 665–674

  • Zhu J, Wang H, Tsou B, Zhu M (2009) Multi-aspect opinion polling from textual reviews. In: Proceedings of conference on information and knowledge management (CIKM), pp 1799–1802

  • Zhuang L, Jing F, Zhu XY (2006) Movie review mining and summarization. In: Proceedings of conference on information and knowledge management (CIKM), pp 43–50

  • Zong B, Song Q, Min W M Rand Cheng, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: Proceedings of international conference on learning representations (ICLR)

Download references

Author information

Authors and Affiliations

  1. RMIT University, Melbourne, Australia

    Yassien Shaalan, Xiuzhen Zhang & Jeffrey Chan

  2. Monash University, Melbourne, Australia

    Mahsa Salehi

Authors
  1. Yassien Shaalan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuzhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahsa Salehi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yassien Shaalan.

Additional information

Responsible editor: Johannes Fürnkranz.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shaalan, Y., Zhang, X., Chan, J. et al. Detecting singleton spams in reviews via learning deep anomalous temporal aspect-sentiment patterns. Data Min Knowl Disc 35, 450–504 (2021). https://doi.org/10.1007/s10618-020-00725-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-020-00725-5

Keywords

Search

Navigation