Electronic commerce has become a popular shopping mode. To enhance their reputations, attract more customers, and finally obtain more benefits, dishonest sellers often recruit buyers or robots to post a large number of deceptive reviews to mislead users. According to the interpretability of learning results, existing methods for detecting deceptive reviews can be mainly divided into explicit feature-based mining ones and neural network-based implicit feature mining ones. The nature of these works is accurate text classification based on coarse-grained features (e.g., topic, sentence, and document) or fine-grained features (e.g., word). To take full merits of existing approaches, this paper proposes a new framework that explores a method to combine the coarse-grained features and the fine-grained features. In this framework, the coarse-grained implicit semantic features of the topic distribution are learned by the concatenation of a Latent Dirichlet Allocation (LDA) topic model and a 2-layered neural network. The fine-grained implicit semantic features from the word vectors representation of the reviews are parallelly learned by a deep learning framework. Finally, these two granular features are combined and adopted to train a Support Vector Machine (SVM) classifier for detecting whether a review is deceptive or not. To verify the effectiveness and performance of this framework, we derive three models by specifying three popular deep learning models, such as TextCNN, long short-term memory (LSTM), and Bi-directional LSTM (BiLSTM) to learn the fine-grained features. Experimental results on a mixed-domain dataset and balanced/unbalanced in-domain datasets show that all the combination models are superior to the corresponding baseline models considering single features.

A deceptive review detection framework: Combination of coarse and fine-grained features

The integration of human physical processes with the computation has created a new paradigm called Cyber-Physical Systems (CPS). The CPSs control the physical processes by utilizing the computational intelligence to acquire the deep knowledge of the monitored environment. Hence the CPSs are designed to be intelligent to provide highly accurate decisions and appropriate actions promptly. The rapidly growing interconnections between the virtual and physical worlds and the development of new intelligent techniques have created new opportunities for the research for next-generation CPS, that is intelligent cyber-physical systems (iCPS). The iCPS are large-scale software intensive and pervasive systems, which by combining various data sources (both from physical objects and virtual components) and applying intelligence techniques, can efficiently manage real-world processes and offers a broad range of novel applications and services. By equipping physical objects with interfaces to the virtual world, and incorporating intelligent mechanisms to leverage collaboration between these objects, the boundaries between the physical and virtual worlds become blurred. Interactions occurring in the physical world are capable of changing the processinga behavior in the virtual world, in a causal relationship

Intelligent, smart and scalable cyber-physical systems

Social network based filtering of unsolicited messages from e-mails

Twitter, the social network evolving faster and regular usage by millions of people. Spam is defined as unwanted content that appears on online social networks (OSN) sites that are driven by several goals such as to spread advertisements, generate sales, viruses, phishing, or simply just to compromise system reputation. Twitter has gained a lot of popularity throughout the world, it continues to be a very interesting target for spammers and malicious users for spreading spam messages. Several machine learning algorithms exist to detect the spam messages in the OSN but still it is a challenging issue. Not only the significant classifiers used are essential but also the selection of relevant features is also very crucial. Binary and Continuous Feature Engineering (BCFE) analysis is proposed in this paper for better feature selection. The features are evaluated using significance strategies and the best features are selected for effective spam classification.

Cinu C. Kiliroor

Papers

Social network based filtering of unsolicited messages from e-mails

Binary and Continuous Feature Engineering Analysis on Twitter Data Stream for Classification of Spam Messages