Spam review detection using LSTM autoencoder: an unsupervised approach
The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writin...
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| Vydáno v: | Electronic commerce research Ročník 22; číslo 1; s. 113 - 133 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.03.2022
Springer Springer Nature B.V |
| Témata: | |
| ISSN: | 1389-5753, 1572-9362 |
| On-line přístup: | Získat plný text |
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| Abstract | The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writing negative reviews. Traditionally, the spam review identification task is seen as a two-class classification problem. The classification approach requires a labelled dataset to train a model for the environment it is working on. The unavailability of the labelled dataset is a major limitation in the classification approach. To overcome the problem of the labelled dataset, we propose an unsupervised learning model combining long short-term memory (LSTM) networks and autoencoder (LSTM-autoencoder) to distinguish spam reviews from other real reviews. The said model is trained to learn the patterns of real review from the review’s textual details without any label. The experimental results show that our model is able to separate the real and spam review with good accuracy. |
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| AbstractList | The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writing negative reviews. Traditionally, the spam review identification task is seen as a two-class classification problem. The classification approach requires a labelled dataset to train a model for the environment it is working on. The unavailability of the labelled dataset is a major limitation in the classification approach. To overcome the problem of the labelled dataset, we propose an unsupervised learning model combining long short-term memory (LSTM) networks and autoencoder (LSTM-autoencoder) to distinguish spam reviews from other real reviews. The said model is trained to learn the patterns of real review from the review’s textual details without any label. The experimental results show that our model is able to separate the real and spam review with good accuracy. |
| Audience | Academic |
| Author | Singh, Jyoti Prakash Saumya, Sunil |
| Author_xml | – sequence: 1 givenname: Sunil orcidid: 0000-0002-9555-9434 surname: Saumya fullname: Saumya, Sunil email: sunil.saumya@iiitdwd.ac.in organization: Indian Institute of Information Technology Dharwad – sequence: 2 givenname: Jyoti Prakash surname: Singh fullname: Singh, Jyoti Prakash organization: National Institute of Technology Patna |
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| Cites_doi | 10.1016/j.ins.2017.01.015 10.1145/2070710.2070716 10.1007/s10489-018-1142-1 10.1016/j.neucom.2016.10.080 10.1186/s40537-015-0029-9 10.1007/s40012-018-0193-0 10.1016/j.future.2019.09.001 10.1016/j.eswa.2016.03.020 10.1007/s00500-019-03851-5 10.12733/jics20105452 10.1016/j.jbusres.2016.08.008 10.1007/s11948-017-9959-2 10.1093/comjnl/bxv068 10.1016/j.eswa.2014.12.029 10.1142/S0219649217500368 10.1016/j.elerap.2018.03.008 10.1016/j.ipm.2018.03.007 10.1145/2783258.2783370 10.1109/ICDM.2007.68 10.1109/ICMLA.2015.37 10.1145/1871437.1871557 10.1145/2339530.2339662 10.1145/1871437.1871669 10.1145/2487575.2487580 10.1145/1242572.1242759 10.1145/2701126.2701130 10.1109/ICEBE.2010.47 10.1145/3038912.3052582 10.21437/Interspeech.2012-65 10.1109/ICDMW.2010.30 10.1145/1531914.1531924 10.1145/1341531.1341560 10.1145/2464464.2464470 10.1145/2187836.2187863 |
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| Keywords | Long short-term memory E-commerce Autoencoder neural network Spam detection Unsupervised learning Machine learning |
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| References | Saumya, Singh (CR33) 2018; 6 Wang, Hou, Song, Li, Kong (CR40) 2016; 59 Saumya, Singh, Dwivedi (CR35) 2019 CR18 Wang, Xie, Liu, Yu (CR38) 2012; 3 CR16 CR15 CR37 Heydari, Tavakoli, Salim (CR9) 2016; 58 CR13 CR12 CR11 Crawford, Khoshgoftaar, Prusa, Richter, Al Najada (CR5) 2015; 2 CR10 CR31 Wang, Gu, Xu (CR39) 2018; 48 Heydari, ali Tavakoli, Salim, Heydari (CR8) 2015; 42 Singh, Irani, Rana, Dwivedi, Saumya, Roy (CR36) 2017; 70 Zhang, Du, Yoshida, Wang (CR42) 2018; 54 CR2 CR4 Li, Qin, Ren, Liu (CR20) 2017; 254 CR3 Kolhar (CR14) 2018; 24 CR6 Li, Lin, Zhang, Li, Zhao (CR21) 2015; 12 Ren, Ji (CR30) 2017; 385 CR7 CR29 Akoglu, Chandy, Faloutsos (CR1) 2013; 13 CR27 CR26 CR25 CR24 Li, Ott, Cardie, Hovy (CR19) 2014; 1 CR23 CR22 CR41 Rastogi, Mehrotra (CR28) 2017; 16 Lau, Liao, Kwok, Xu, Xia, Li (CR17) 2011; 2 Roy, Singh, Banerjee (CR32) 2020; 102 Saumya, Singh, Baabdullah, Rana, Dwivedi (CR34) 2018; 29 RY Lau (9413_CR17) 2011; 2 9413_CR41 W Zhang (9413_CR42) 2018; 54 Z Wang (9413_CR39) 2018; 48 9413_CR29 S Saumya (9413_CR35) 2019 9413_CR25 9413_CR7 9413_CR26 Z Wang (9413_CR40) 2016; 59 9413_CR6 9413_CR27 9413_CR4 Y Li (9413_CR21) 2015; 12 9413_CR3 9413_CR22 JP Singh (9413_CR36) 2017; 70 9413_CR2 9413_CR23 M Kolhar (9413_CR14) 2018; 24 9413_CR24 M Crawford (9413_CR5) 2015; 2 J Li (9413_CR19) 2014; 1 9413_CR31 A Heydari (9413_CR9) 2016; 58 G Wang (9413_CR38) 2012; 3 S Saumya (9413_CR34) 2018; 29 A Heydari (9413_CR8) 2015; 42 9413_CR18 Y Ren (9413_CR30) 2017; 385 PK Roy (9413_CR32) 2020; 102 9413_CR15 A Rastogi (9413_CR28) 2017; 16 9413_CR37 L Akoglu (9413_CR1) 2013; 13 9413_CR16 L Li (9413_CR20) 2017; 254 9413_CR10 9413_CR11 S Saumya (9413_CR33) 2018; 6 9413_CR12 9413_CR13 |
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| Title | Spam review detection using LSTM autoencoder: an unsupervised approach |
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