Deceptive Reviews Detection Using Deep Learning Techniques

Jain, Neha; Kumar, Abhay; Singh, Shekhar; Singh, Chirag; Tripathi, Suraj

doi:10.1007/978-3-030-23281-8_7

Cited by 24 publications

(7 citation statements)

References 12 publications

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“…Deceptive parts of long documents may also be lost when truncating. In such cases, models capable of processing longer texts can be considered, such as hierarchical RNNs (Chalkidis et al 2019;Jain et al 2019) or multi instance learning as in (Jain et al 2019). No truncation was necessary in our logistic regression experiments, but long texts may still be a problem, at least in principle.…”

Section: [Storage Area] [And Not]mentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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Automatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.

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Section: [Storage Area] [And Not]mentioning

confidence: 99%

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

et al. 2021

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“…Multilayer perceptron (MLP) and convolutional neural network (CNN) are used to learn feature vectors, and language features and behavioral features are weighted through attention mechanisms. Jain et al [27] have proposed two different methods -multi-instance learning and hierarchical architecture to handle the variable length review texts. Experimental results on multiple benchmark datasets of deceptive reviews performed well.…”

Section: B Identify Fake Reviews From the Reviewer's Behavior Characmentioning

confidence: 99%

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

et al. 2020

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Fake reviews may mislead consumers. A large number of fake reviews will even cause huge property losses and public opinion crises. Therefore, it is necessary to detect and filter fake reviews. However, most existing methods have lower accuracy in detecting fake reviews due to they just use single features and lack of labeled experimental data. To solve this problem, we propose a novelty method to detect fake reviews based on multiple feature fusion and rolling collaborative training. First, the method requires an initial index system with multiple features such as text features, sentiment features of reviews and behavior features of reviewers. Second, the method needs an initial training sample set. Thus, we designed related algorithms to extract all the features of a review. Then the classification of the review is labeled manually. Finally, the method uses the initial sample set to train 7 classifiers, and the most accurate classifier will be selected to classify new reviews. The novelty of the method lies in that the features and the classification labels of the new reviews will be added into the initial sample set as new samples. So the size of the sample set will increase automatically. The experimental results in the reviews of yelp shopping website show that the accuracy of the proposed method for detecting fake reviews is 84.45%, which is 3.5% higher than the baseline methods. And compared with the latest deep learning model, its baseline precision has increased by 5.3%. According to the Friedman test, the support vector machine (SVM) classifier and random forest (RF) classifier has been proven to be the best one by statistical means. It means our method which uses multiple features has higher accuracy than the baseline models. Meanwhile, it also resolves the problem of lacking labeled training samples in fake reviews detection. INDEX TERMS Fake review detection, machine learning, multiple feature fusion, feature extraction, rolling collaborative training

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“…In training a CNN model for an image, gradient descent is applicable to find the local minimum of the loss function [236]. The convolution and spooling functions in CNN are what make CNN superior in image processing because it pays attention to the locality of reference problems that other neural network methods do not do [237]. Thus causing other neural network methods to experience the curse of dimensionality problem [238].…”

Section: ) Deep Learningmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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Deceptive Reviews Detection Using Deep Learning Techniques

Cited by 24 publications

References 12 publications

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Deception detection in text and its relation to the cultural dimension of individualism/collectivism

Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

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