An effective hybrid Cuckoo Search with Harmony search for review spam detection

Rajamohana, S. P.; Umamaheswari, K.; Keerthana, S.Vasantha

doi:10.1109/aeeicb.2017.7972369

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…At the same time, their combination gave 84% and 74% with NB and SMO. However, the results did not exceed 85%-furthermore, Rajamohana et al [22] proposed a methodology for detecting opinion spam using features detection. They proposed an approach that deals with selecting subset features from many feature sets for the classifier to separate spam or ham.…”

Section: Literature Reviewmentioning

confidence: 97%

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The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Tufail

Ashraf²,

Alsubhi

et al. 2022

IEEE Access

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The outbreak of Covid-19 and the enforcement of lockdown, social distancing, and other precautionary measures lead to a global increase in online shopping. The increasing significance of online shopping and extensive use of e-commerce has increased competition between companies for online selling. Highlights that online reviews play a significant role in boosting a business or slandering it. Product review is an essential factor in customers' decision-making, leading to an intense topic known as fraudulent or fake reviews detection. Given these reviews' power over a business, the treacherous acts of giving false reviews for personal gains have increased with time. In our research, we proposed a fake review detection model by using Text Classification and techniques related to Machine Learning. We used classifiers such as Support Vector Machine, K-Nearest Neighbor, and logistic regression (SKL), using a bigram model that detects fraudulent reviews based on the number of pronouns, verbs, and sentiments. Our proposed methodology for detecting fake online reviews outperforms on the yelp dataset and the TripAdvisor dataset compared to other state-of-the-art techniques with 95% and 89.03% accuracy.INDEX TERMS Fake reviews, K-Nearest Neighbor (KNN), machine learning, natural language processing, sentiment analysis, support vector machine (SVM).

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Section: Literature Reviewmentioning

confidence: 97%

“…The proposed algorithm further demonstrated using sample review's data sets and amazon data sets, achieving an accuracy of 80.77%. S.P.Rajamohana et al [22] came up with a feature selection technique that was effective. It is called a cuckoo search in junction with harmony search.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Tufail

Ashraf²,

Alsubhi

et al. 2022

IEEE Access

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“…Many unsupervised learning techniques have been used in spam detection which are: Natural Language Processing [6,9][58] Markov Network [25], Neural Auto-encoder Decision Forest [16]¸ and PU Learning [26]. Other than these supervised and unsupervised learning techniques, there are many other techniques that have been used for spam detection such as Fuzzy Logic [27], Heterogeneous Information Network [28], Hadoop [29], Text Mining [30], Sentiment Analysis [31][32][33][34][35], Cuckoo Search [36] [57], Adaptive Binary Flower Pollination [37], and Map Reduce [29]. Spam Review Detection has been the most active area of research in past years that covers all broad.…”

Section: Figure 1 Types Of Spammentioning

confidence: 99%

Spam Review Detection:A Systematic Literature Review

Farooq¹

2020

Preprint

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In this era of technology, people rely on online posted reviews before buying any product. These reviews are very important for both the consumers and people. Consumers and people use this information for decision making while buying products or investing money in any product. This has inclined the spammers to generate spam or fake reviews so that they can recommend their products and beat the competitors. Spammers have developed many systems to generate the bulk of spam reviews within hours. Many techniques, strategies have been designed and recommended to resolve the issue of spam reviews. In this paper, a complete review of existing techniques and strategies for detecting spam review is discussed. Apart from reviewing the state-of-the-art research studies on spam review detection, a taxonomy on techniques of machine learning for spam review detection has been proposed. Moreover, its focus on research gaps and future recommendations for spam review identification.

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“…is algorithm can also detect multitype spammers by recognizing only a tiny percentage of positive data, ideal for realworld applications. A hybrid method for feature selection was proposed by Rajamohana et al [36], which uses cuckoo search (CS) along with harmony search to increase the processing rate and prediction accuracy. e Naive Bayes classifier was employed to classify the review into spam and nonspam.…”

Section: Introductionmentioning

confidence: 99%

Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

Shringi

Sharma

Suthar

2022

Mathematical Problems in Engineering

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Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies.

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An effective hybrid Cuckoo Search with Harmony search for review spam detection

Cited by 18 publications

References 10 publications

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

The Effect of Fake Reviews on e-Commerce During and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection

Spam Review Detection:A Systematic Literature Review

Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

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