Opinion spam and analysis

Mining and summarizing customer reviews

Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793

Thumbs up?: sentiment classification using machine learning techniques

Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

Peter, P., & Turney (2002) · Cited by 3,646

Thumbs up or thumbs down?

Turney, P. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073153 · Cited by 2,354

Mining the peanut gallery

Dave, K. S., Lawrence, S., & Pennock, D. M. (2003) · International World Wide Web Conference (WWW) · doi:10.1145/775152.775226 · Cited by 1,925

On the resemblance and containment of documents

Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818

Extracting product features and opinions from reviews

Popescu, A.-M., & Etzioni, O. (2005) · doi:10.3115/1220575.1220618 · Cited by 1,714

A Bayesian Approach to Filtering Junk E-Mail

Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,212

Web data mining: exploring hyperlinks, contents, and usage data

(2012) · Choice Reviews Online · doi:10.5860/choice.49-2718 · Cited by 1,181

Mining the Web: Discovering Knowledge from Hypertext Data

Chakrabarti, S. (2002) · Thammasat University Digital Collections · Cited by 696

Detecting Fake Review with Rumor Model—Case Study in Hotel Review

Chang, T., Hsu, P. Y., Cheng, M. S., Chung, C.-Y., & Chung, Y.-L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23862-3_18 · Cited by 15

Collusive Opinion Fraud Detection in Online Reviews

Xu, C., & Zhang, J. (2017) · ACM Transactions on the Web · doi:10.1145/3098859 · Cited by 16

Detecting spam reviewers by combing reviewer feature and relationship

Liang, D., Liu, X., & Shen, H. (2014) · International Conference on Informative and Cybernetics for Computational Social Systems · doi:10.1109/iccss.2014.6961824 · Cited by 15

Recital of supervised learning on review spam detection: An empirical analysis

Khurshid, F., Zhu, Y., Yohannese, C. W., & Iqbal, M. (2017) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske.2017.8258755 · Cited by 16

Extracting Customer Reviews from Online Shopping and Its Perspective on Product Design

Anh, K. Q., Nagai, Y., & Nguyen, L.-M. (2019) · Vietnam Journal of Computer Science · doi:10.1142/s2196888819500088 · Cited by 11

Technologies for Reusing Text from the Web

Potthast, M. (2012) · Common Library Network (Der Gemeinsame Bibliotheksverbund) · doi:10.25643/bauhaus-universitaet.1566 · Cited by 15

From Opinions to Recommendations

O’Mahony, M. P., & Smyth, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-90092-6_13 · Cited by 14

Collusive shill bidding detection in online auctions using Markov Random Field

Majadi, N., Trevathan, J., & Bergmann, N. W. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2019.100831 · Cited by 14

Review Manipulation: Literature Review, and Future Research Agenda

Ansari, S., & Gupta, S. (2021) · Pacific Asia journal of the Association for Information Systems · doi:10.17705/1pais.13104 · Cited by 18

Detecting fake reviewers in heterogeneous networks of buyers and sellers: a collaborative training-based spammer group algorithm

Zhang, Q., Liang, Z., Ji, S., Xing, B., & Chiu, D. K. W. (2023) · Cybersecurity · doi:10.1186/s42400-023-00159-8 · Cited by 16

Bengali fake reviews: A benchmark dataset and detection system

Shahariar, G. M., Shawon, M. T. R., Shah, F. M., Alam, M. S., & Mahbub, M. S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127732 · Cited by 18

LINQ: A Framework for Location-Aware Indexing and Query Processing

Liu, X., Chen, L., & Wan, C. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2365792 · Cited by 14

A deceptive detection model based on topic, sentiment, and sentence structure information

Du, X., Zhu, R., Zhao, F., Zhao, F., Han, P., & Zhu, Z. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01779-0 · Cited by 14

A proposed system for opinion mining using machine learning, NLP and classifiers

Tanwar, P., & Rai, P. (2020) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v9.i4.pp726-733 · Cited by 15

Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms

Danilchenko, K., Segal, M., & Vilenchik, D. (2022) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v16i1.19278 · Cited by 17

Confidence-Aware Sentiment Quantification via Sentiment Perturbation Modeling

Tang, X., Liao, D., Shen, M., Zhu, L., Huang, S., Li, G., Man, H., & Xu, J. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3301956 · Cited by 14

Detecting Negative Deceptive Opinion from Tweets

Molla, A., Biadgie, Y., & Sohn, K.-A. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-5281-1_36 · Cited by 14

An Analysis of E-Commerce Identification Using Sentimental Analysis: A Survey

Pandiaraja, P., Aishwarya, S., Indubala, S. V., Neethiga, S., & Sanjana, K. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-2719-5_69 · Cited by 13

DHMFRD – TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

Duma, R. A., Niu, Z., Nyamawe, A. S., Tchaye-Kondi, J., Chambua, J., & Yusuf, A. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15193-4 · Cited by 13

Rethinking Unsupervised Graph Anomaly Detection With Deep Learning: Residuals and Objectives

Ma, X., Liu, F., Wu, J. X., Yang, J., Xue, S., & Sheng, Q. Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3501307 · Cited by 17

An Empirical Study of Online Consumer Review Spam: A Design Science Approach

Lau, R. Y. K., Liao, S. S., & Xu, K. S. J. (2010) · International Conference on Information Systems · Cited by 13

An investigation of online review helpfulness based on movie reviews

Liu, Z., Li, H., & Liu, L. (2014) · AFRICAN JOURNAL OF BUSINESS MANAGEMENT · doi:10.5897/ajbm11.2628 · Cited by 13

An Efficient Feature Selection Technique of Unsupervised Learning Approach for Analyzing Web Opinions

Valli, M. S., & Arasu, G. T. (2016) · Cited by 13

Identifying Helpful Online Reviews with Word Embedding Features

Chen, J., Zhang, C., & Niu, Z. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47650-6_10 · Cited by 13

Cooks Distance and Mahanabolis Distance Outlier Detection Methods to identify Review Spam

Algur, S. P. (2017) · International Journal Of Engineering And Computer Science · doi:10.18535/ijecs/v6i6.16 · Cited by 13

An ensemble method for the credibility assessment of user-generated content

Fontanarava, J., Pasi, G., & Viviani, M. (2017) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1145/3106426.3106464 · Cited by 16

Open Set Text Classification Using CNNs

Prakhya, S., Venkataram, V., & Kalita, J. K. (2017) · International conference natural language processing · Cited by 30

ReviewChain

Martens, D., & Maalej, W. (2018) · IEEE/ACM International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB) · doi:10.1145/3194113.3194120 · Cited by 13

Detecting Product Review Spammers Using Principles of Big Data

Rout, J. K., Dalmia, A., Rath, S. K., Mohanta, B. K., Ramasubbareddy, S., & Gandomi, A. H. (2021) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3097805 · Cited by 13

Detecting Fake Reviews using Machine learning techniques: a survey

Agarwal, R., & Sharma, D. K. (2022) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite53722.2022.9823633 · Cited by 14

Tri-Training for authorship attribution with limited training data: a comprehensive study

Qian, T., Liu, B., Chen, L., Peng, Z., Zhong, M., He, G., Li, X., & Xu, G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.064 · Cited by 13

Towards bounding sequential patterns

Raïssi, C., & Pei, J. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020612 · Cited by 13

Learning to Estimate Slide Comprehension in Classrooms with Support Vector Machines

Pattanasri, N., Mukunoki, M., & Minoh, M. (2011) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2011.22 · Cited by 13

Nebular: A sentiment classification system for the tourism business

Palakvangsa-Na-Ayudhya, S., Sriarunrungreung, V., Thongprasan, P., & Porcharoen, S. (2011) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse.2011.5930137 · Cited by 13

Diversionary comments under political blog posts

Wang, J., Yu, C. T., Yu, P. S., Liu, B., & Meng, W. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398518 · Cited by 13

Sentiment Analysis in Social Streams

Saif, H., Ortega, F. J., Fernández, M., & Cantador, I. (2016) · Human-computer interaction series · doi:10.1007/978-3-319-31413-6_7 · Cited by 14

Psycholinguistic Features for Deceptive Role Detection in Werewolf

Girlea, C., Gîrju, R., & Eyal Amir, A. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1047 · Cited by 13

Web Mining: Opinion and Feedback Analysis for Educational Institutions

PrakashVerma, J., Patel, B., & Patel, A. M. (2013) · International Journal of Computer Applications · doi:10.5120/14579-2800 · Cited by 13

Extracting Topic-related Opinions and their Targets in NTCIR-7.

Kim, Y., Kim, S., & Myaeng, S.-H. (2008) · Cited by 13

Modeling Review Spam Using Temporal Patterns and Co-bursting BehaviorsPreprint

Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.06625 · Cited by 20

Extended Recommendation Framework: Generating the Text of a User Review as a Personalized SummaryPreprint

Poussevin, M., Guigue, V., & Gallinari, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.5448 · Cited by 13

Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

(2014) · doi:10.3115/v1/w14-27 · Cited by 13

A Hybridized Approach for Enhanced Fake Review Detection

Xu, S., Cuan, H., Yin, Z., & Yin, C. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3411635 · Cited by 15

Fake Review Detection using Classification

Neha, S., & Anala, A. (2018) · International Journal of Computer Applications · doi:10.5120/ijca2018917316 · Cited by 12

Fake Reviewer Group Detection in Online Review Systems

Cao, C., Li, S., Yu, S., & Chen, Z. (2021) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw53433.2021.00122 · Cited by 15

Aspect Oriented Sentiment Analysis on Customer Reviews on Restaurant Using the LDA and BERT Method

Lohith, C., Chandramouli, H., Balasingam, U., & Kumar, S. A. (2023) · SN Computer Science · doi:10.1007/s42979-022-01634-8 · Cited by 14

Detecting Professional Spam Reviewers

Huang, J., Qian, T., He, G., Zhong, M., & Peng, Q. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53917-6_26 · Cited by 12

Ordering adverbs by their scaling effect on adjective intensity

Ruppenhofer, J., Brandes, J., Steiner, P., & Wiegand, M. (2015) · ERef Bayreuth (University of Bayreuth) · Cited by 12

Beyond mean rating: Probabilistic aggregation of star ratings based on helpfulness

Tay, W., Zhang, X., & Karimi, S. (2019) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24297 · Cited by 12

Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment

Stelmakh, I., Shah, N. B., & Singh, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16611 · Cited by 14

Adaptive subjective triggers for opinionated document retrieval

Seki, K., & Uehara, K. (2009) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/1498759.1498805 · Cited by 12

Detecting Linkedin Spammers and its Spam Nets

M., V. ́., A´, M., & Cacheda, F. (2013) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2013.040930 · Cited by 12

A new web personalization decision-support artifact for utility-sensitive customer review analysis

Flory, L., Osei-Bryson, K.-M., & Thomas, M. A. (2016) · Decision Support Systems · doi:10.1016/j.dss.2016.11.003 · Cited by 13

Improving item-based recommendation accuracy with user's preferences on Apache Mahout

Jabakji, A., & Dağ, H. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840789 · Cited by 12

A fuzzy approach to detect spammer groups

Do, Q. N. T., Hussain, F. K., & Nguyen, B. T. (2017) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2017.8015478 · Cited by 12

Sentiment Detection, Recognition and Aspect Identification

Faiz, S., & Dahab, M. Y. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017915675 · Cited by 12

Cyber Social Media Analytics and Issues: A Pragmatic Approach for Twitter Sentiment Analysis

Sharma, S., & Jain, A. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-6861-5_41 · Cited by 12

Detect Review Manipulation by Leveraging Reviewer Historical Stylometrics in Amazon, Yelp, Facebook and Google Reviews

Sadman, N., Gupta, K. D., Haque, A., Poudyal, S., & Sen, S. (2020) · International Conference on E-Business and Applications (ICEBA) · doi:10.1145/3387263.3387272 · Cited by 12

An overview of soft computing techniques on Review Spam Detection

Amin, I., & Dubey, M. K. (2021) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem51511.2021.9445280 · Cited by 12

Improved Fake Reviews Detection Model Based on Vertical Ensemble Tri-Training and Active Learning

Yin, C., Cuan, H., Zhu, Y., & Yin, Z. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3450285 · Cited by 12

Opinion spam detection using Deep Learning

Bathla, G., & Kumar, A. (2021) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin52536.2021.9566048 · Cited by 12

Detecting Fake Reviews of Hype About Restaurants by Sentiment Analysis

Chen, R. Y., Guo, J. Y., & Deng, X. L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11538-2_3 · Cited by 12

Sentiment retrieval on web reviews using spontaneous natural speech

Pereira, J. C., Luque, J., & Anguera, X. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854470 · Cited by 12

GANs for Semi-Supervised Opinion Spam Detection

Stanton, G., & Irissappane, A. A. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/723 · Cited by 14

Does algorithmic recommendation complement or substitute advertising and influencers? Consumer attitudes toward recommendation information and the formation of purchase intentions

Aoki, T., & Matsui, A. (2025) · Computers in Human Behavior · doi:10.1016/j.chb.2025.108735 · Cited by 15

Whose reviews are most valuable for predicting the default risk of peer-to-peer lending platforms? Evidence from China

Li, L., Zheng, H., Chen, D., & Zhu, B. (2022) · Electronic Commerce Research · doi:10.1007/s10660-022-09571-7 · Cited by 11

Detecting Spam Reviews on Vietnamese E-Commerce Websites

Dinh, C. V., Luu, S. T., & Nguyen, A. G.-T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_48 · Cited by 14

A Study on Fake Review Detection Based on RoBERTa and Behavioral Features

Liu, J., Quan, P., & Zhang, W. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.08.131 · Cited by 11

Semi-Supervised Based Bangla Fake Review Detection: A Comparative Analysis

Absar, N., Mahmud, T., Hanip, A., & Hossain, M. S. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11005278 · Cited by 11

Linking Neuroimaging with Functional Linguistic Analysis to Understand Processes of Successful Communication

O’Donnell, M. B., & Falk, E. B. (2015) · Communication Methods and Measures · doi:10.1080/19312458.2014.999751 · Cited by 14

Contextual Sequential Pattern Mining

Rabatel, J., Bringay, S., & Pascal Poncelet, B. (2010) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2010.182 · Cited by 11

Online Review Spam Detection using Language Model and Feature Selection

S.Patil, M., & Bagade, A. M. (2012) · International Journal of Computer Applications · doi:10.5120/9562-4031 · Cited by 12

Overview of Web Spammer Detection

Mo, Q., & Yang, K. (2014) · Cited by 12

Helping consumers to overcome information overload with a diversified online review subset

Jin, Z., Zhangwen, W., & Naichen, N. (2019) · Frontiers of Business Research in China · doi:10.1186/s11782-019-0062-1 · Cited by 11

A Study on Identification of Important Features for Efficient Detection of Fake Reviews

Lin, T.-Y., Chakraborty, B., & Peng, C. (2021) · International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi53623.2021.9655845 · Cited by 11

Detection of Chinese Deceptive Reviews Based on Pre-Trained Language Model

Weng, C.-H., Lin, K.-C., & Ying, J. J.-C. (2022) · Applied Sciences · doi:10.3390/app12073338 · Cited by 11

Fake Reviews Classification using Deep Learning

Ashraf, S., Rehman, F. U., Sharif, H., Kirn, H., Arshad, H., & Manzoor, H. (2023) · International Multi-disciplinary Conference in Emerging Research Trends (IMCERT) · doi:10.1109/imcert57083.2023.10075156 · Cited by 12

Detecting Fake Reviews in Google Maps—A Case Study

Gryka, P., & Janicki, A. (2023) · Applied Sciences · doi:10.3390/app13106331 · Cited by 11

Next-Word Prediction: A Perspective of Energy-Aware Distributed Inference

Wang, S., Shao, Z., & Lui, J. C. S. (2023) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2023.3310536 · Cited by 12

Attention aux faux avis! Investigations expérimentales sur les indicateurs contextuels facilitant la détection des avis trompeurs par les internautes

Munzel, A. (2015) · Recherche et Applications en Marketing (French Edition) · doi:10.1177/0767370115584314 · Cited by 12

Load balancing for partition-based similarity search

Tang, X., Alabduljalil, M., Jin, X., & Yang, T. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609624 · Cited by 11

Evaluation of Data Mining Features, Features Taxonomies and their Applications

Noekhah, S., Salim, N. B., & Zakaria, N. H. (2017) · Kurdistan Journal of Applied Research · doi:10.24017/science.2017.3.3 · Cited by 11

Estimating the Rating of the Reviews Based on the Text

Kavousi, M., & Saadatmand, S. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-13-2514-4_22 · Cited by 11

Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions

Luo, S., & Wan, S. (2019) · IEEE Access · doi:10.1109/access.2019.2891907 · Cited by 11

Robustness of Word and Character N-gram Combinations in Detecting Deceptive and Truthful Opinions

Siagian, A. H. A. M., & Aritsugi, M. (2020) · Journal of Data and Information Quality · doi:10.1145/3349536 · Cited by 11

Improving Explainable Recommendations by Deep Review-Based Explanations

Ouyang, S., & Lawlor, A. (2021) · IEEE Access · doi:10.1109/access.2021.3076146 · Cited by 11

An Exploratory Analysis on the Unfold of Fake News During COVID-19 Pandemic

Gupta, A., Bansal, A., Mamgain, K., & Gupta, A. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-2877-1_24 · Cited by 11

Non parametric statistical models for on-line text classification

Cerchiello, P., & Giudici, P. (2012) · Advances in Data Analysis and Classification · doi:10.1007/s11634-012-0122-2 · Cited by 11

Effectiveness of web search results for genre and sentiment classification

Na, J., & Thet, T. T. (2009) · Journal of Information Science · doi:10.1177/0165551509104233 · Cited by 11

Opinion Spam Recognition Method for Online Reviews using Ontological Features

Long, N. H., Nghia, P. H. T., & Vuong, N. M. (2018) · Tạp chí Khoa học · doi:10.48550/arxiv.1807.11024 · Cited by 13

Mining Weak Relations Between Reviews for Opinion Spam Detection

Xu, Y., Liu, H., Ge, J., Zhang, X., Hu, J., Wu, Y., Lv, H., Shi, H., & Zhou, W. J. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3221008 · Cited by 10

Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

Shawon, M. T. R., Shahariar, G. M., Shah, F. M., Alam, M. S., & Mahbub, M. S. (2023) · doi:10.1109/icnlp58431.2023.00011 · Cited by 13

Deceptive opinion spam detection using feature reduction techniques

Maurya, S. K., Singh, D., & Maurya, A. K. (2023) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-023-02208-4 · Cited by 10

Leveraging Stacking Framework for Fake Review Detection in the Hospitality Sector

Ashraf, S. A., Javed, A. F., Bellary, S., Bala, P. K., & Panigrahi, P. K. (2024) · Journal of theoretical and applied electronic commerce research · doi:10.3390/jtaer19020075 · Cited by 12

Review Authorship Attribution in a Similarity Space

Qian, T., Liu, B., Li, Q., & Si, J. (2015) · Journal of Computer Science and Technology · doi:10.1007/s11390-015-1513-6 · Cited by 10

Opinion mining framework using proposed RB-bayes model for text classication

Bhalla, R., & Bagga, A. (2019) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v9i1.pp477-484 · Cited by 10

Understanding Users’ Satisfaction towards Public Transit System in India: A Case-Study of Mumbai

Das, R. D. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10030155 · Cited by 10

If a Picture is Worth 1000 Words, Is a Word Worth 1000 Features for Design Metric Estimation?

Edwards, K. M., Peng, A., Miller, S. R., & Ahmed, F. (2021) · Journal of Mechanical Design · doi:10.1115/1.4053130 · Cited by 10

A hierarchical neural network model with user and product attention for deceptive reviews detection

Ren, Y., Yan, M., & Ji, D. (2022) · Information Sciences · doi:10.1016/j.ins.2022.05.011 · Cited by 10

Analyzing and Detecting Review Spam

Jindal, N., & Liu, B. (2007) · Proceedings · doi:10.1109/icdm.2007.68 · Cited by 226

Collective Opinion Spam Detection: Bridging Review Networks and Metadata

Rayana, S., & Akoglu, L. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783370 · Cited by 604

Opinion Spam Detection in Online Reviews

Rastogi, A., & Mehrotra, M. (2017) · Journal of Information & Knowledge Management · doi:10.1142/s0219649217500368 · Cited by 51

Opinion mining and opinion spam: A literature review focusing on product reviews

Sheibani, A. A. (2012) · International Symposium on Telecommunications · doi:10.1109/istel.2012.6483152 · Cited by 10

Opinion Spam Detection

Bensouda, N., Fkihi, S. E., & Faizi, R. (2018) · International Conference on Learning and Optimization Algorithms: Theory and Applications (LOPAL) · doi:10.1145/3230905.3230922 · Cited by 4