Naznin Sultana

Work place: Department of Information Technology, Malaysia University of Science & Technology, Petaling Jaya, Malaysia



Research Interests: Computational Learning Theory, Image Compression, Image Manipulation, Data Mining, Data Compression, Data Structures and Algorithms


Naznin Sultana received her Master’s degree in Computer Science and Engi-neering from Jahangirnagar University, Bangladesh and completed her B.Sc. degree from the same institution in Elec-tronics and Computer Science. Recently she has been admitted in PhD in Infor-matics in Malaysia University of Sci-ence and Technology, Malaysia.
She is currently servicing as an Assistant Professor in the de-partment of Computer Science and Engineering at Daffodil International University in Bangladesh. Previously she served as a faculty member in Computer Science and Engineering de-partment at two other reputed universities in Bangladesh. Her research interest includes Data Mining, Machine Learning, Im-age Processing and IoT.
Ms. Sultana is a member of Bangladesh Computer Society.

Author Articles
Deceptive Opinion Detection Using Machine Learning Techniques

By Naznin Sultana Sellappan Palaniappan

DOI:, Pub. Date: 8 Feb. 2020

Nowadays, online reviews have become a valuable resource for customer decision making before purchasing a product. Research shows that most of the people look at online reviews before purchasing any product. So, customers reviews are now become a crucial part of doing business online. Since review can either promote or demote a product or a service, so buying and selling fake reviews turns into a profitable business for some people now a days. In the past few years, deceptive review detection has attracted significant attention from both the industrial organizations and academic communities. However, the issue remains to be a challenging problem due to the lack of labeled dataset for supervised learning and evaluation. Also, study shows that both the state of the art computational approaches and human readers acquire an error rate of about 35% to 48% in identifying fake reviews. This study thoroughly investigated and analyzed customers’ online reviews for deception detection using different supervised machine learning methods and proposes a machine learning model using stochastic gradient descent algorithm for the detection of spam review. To reduce bias and variance, bagging and boosting approach was integrated into the model. Furthermore, to select the most appropriate features in the feature selection step, some rules using regular expression were also generated. Experiments on hotel review dataset demonstrate the effectiveness of the proposed approach.

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