Abstract
Opinion mining, an upcoming area of computational linguistics, deals with analysis of public sentiment polarity of a product giving rise to a new platform on social media and e-commerce for users to express/explore opinions on products and/or product manufacturers for the betterment of the product development. A tentative product rating can be derived from the product opinions mined. ALOSI takes opinion mining to a deeper level performing opinion mining at aspect level giving well-analyzed results at product component level. But the data received is not always totally relevant leading to false rating of the product or aspect(s). This irrelevant data can be labeled as spam and is important to identify as it may restrict the model. The presented work models a product-based aspect-level sentiment analysis on public reviews with spam identification and filtering, hence, highlighting the difference in the rating of product or aspect(s) with the presence and absence of spam reviews.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tang D, Qin B, Liu T (2015) Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisc Rev Data Min Knowl Disc 5(6):292–303
Medhat W, Hassan A, Korashy H (2014) Sentiment analysis algorithms and applications: A survey. Ain Shams Eng J 5(4):1093–1113
Shivaprasad TK, Shetty J (2017) Sentiment analysis of product reviews: A review. In 2017 international conference on inventive communication and computational technologies (ICICCT), IEEE, pp 298–301
Serrano-Guerrero J, Olivas JA, Romero FP, Herrera-Viedma E (2015) Sentiment analysis: A review and comparative analysis of web services. Inf Sci 311:18–38
Tsytsarau M, Palpanas T (2012) Survey on mining subjective data on the web. Data Min Knowl Disc 24(3):478–514
Schouten K, Frasincar F (2016) Survey on aspect-level sentiment analysis. IEEE Trans Knowl Data Eng 28(3):813–830
Patil PP, Phansalkar S, Kryssanov VV (2019) Topic modelling for aspect-level sentiment analysis. In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, Springer, Singapore, pp 221–229
Hu M, Liu B (2004) Mining opinion features in customer reviews. AAAI 4(4):755–760
Guang Qiu, Bing Liu, Jiajun Bu and Chun Chen. Expanding domain sentiment lexicon through double propagation. In IJCAI (Vol. 9, pp. 1199–1204), 2009
Hu M, Liu B (2004) Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 168–177
Titov I, McDonald R (2008) Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web. ACM, pp 111–120
Enrique Puertas Sanz (2008) José MarÃa Gómez Hidalgo and José Carlos Cortizo Pérez. Email spam filtering. Adv Comput 74:45–114
Hu X, Tang J, Gao H, Liu H (2014) Social spammer detection with sentiment information. In 2014 IEEE International Conference on Data Mining (ICDM), IEEE, pp 180–189
Shehnepoor S, Salehi M, Farahbakhsh R, Crespi Noel (2017) NetSpam: A network-based spam detection framework for reviews in online social media. IEEE Trans Inf Forensics Secur 12(7):1585–1595
Li J, Cardie C, Li S (2013) Topicspam: a topic-model based approach for spam detection. In Proceedings of the 51st annual meeting of the association for computational linguistics (vol 2 Short Papers), vol 2, pp 217–221
Thanigaivelan NK, Nigussie E, Kanth RK, Virtanen S, Isoaho J (2016) Distributed internal anomaly detection system for Internet-of-Things. In 2016 13th IEEE annual consumer communications & networking conference (CCNC), IEEE, pp. 319–320
Rosso P, Cagnina LC (2017) Deception detection and opinion spam. In A practical guide to sentiment analysis. Springer International Publishing, pp 155–171
Rout JK, Singh S, Jena SK, Bakshi S (2017) Deceptive review detection using labeled and unlabeled data. Multimedia Tools Appl 76(3):3187–3211
Arif MH, Li J, Iqbal M, Liu K (2017) Sentiment analysis and spam detection in short informal text using learning classifier systems. Soft Comput 1–11
Narayan R, Rout JK, Jena SK (2018) Review spam detection using opinion mining. In Progress in intelligent computing techniques: theory, practice, and applications. Springer, Singapore, pp 273–27
Jing L-P, Huang H-K, Shi H-B (2002) Improved feature selection approach TFIDF in text mining. In Proceedings International Conference on Machine Learning and Cybernetics, 2002, vol 2, IEEE, pp 944–946
McCallum A, Nigam K (1998) A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, vol 752, no 1, pp 41–48
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. IJCAI 14(2):1137–1145
Maynard DG, Greenwood MA (2014) Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In LREC, pp 4238–4243
Falcone R, Castelfranchi C (2004) Trust dynamics: How trust is influenced by direct experiences and by trust itself. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004 (AAMAS 2004), IEEE, pp 740–747
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patil, P.P., Phansalkar, S., Ahirrao, S., Pawar, A. (2021). ALOSI: Aspect-Level Opinion Spam Identification. In: Jat, D.S., Shukla, S., Unal, A., Mishra, D.K. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-15-5309-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-5309-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5308-0
Online ISBN: 978-981-15-5309-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)