Detecting Negative Deceptive Opinion from Tweets

Molla, Alemu; Biadgie, Yenewondim; Sohn, Kyung-Ah

doi:10.1007/978-981-10-5281-1_36

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Positive and negative reviews elevate or denigrate specific goods to influence customers' choices and gain a competitive edge. Molla et al [10] used a hybrid approach combining behavior-based features and content. The fake reviews are detected using machine learning classifiers, giving input of 133 unique features generated from a combination of content and behavior-based features.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Alshehri

2024

CMC

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Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, among other things. Online reviews are classified using a semi-supervised approach (PU-learning) since there is a shortage of labeled data, and they are dynamic. Then, classification is performed using the machine learning techniques Support Vector Machine (SVM) and Nave Bayes. The performance of the suggested system has been compared with standard works, and experimental findings are assessed using several assessment metrics.

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Section: Literature Reviewmentioning

confidence: 99%

“…The five classifiers employed are SVM, Naive Bayes, KNN, k-star, and decision trees. To identify fraudulent reviews on the dataset they had gathered, the authors in [10] employed Naive Bayes, Decision trees, SVM, Random forests, and Maximum Entropy classifiers. The authors employed both SVM and Naive base classifiers [11].…”

Section: Introductionmentioning

confidence: 99%

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

Alshehri

2024

CMC

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“…Instead, [28] have tried to exploit emotions as a discriminator between true and misleading opinions without obtaining significant results. In addition, [29] have tried to construct two independent components, one dedicated to misleadingness and one to sentiment, for detecting bogus negative sentiment reviews. Reference [30] additionally showed how the use of sentiment could help to obtain classifiers with lower bias, while [31] showed the presence of correlation of positive sentiment and truthfulness on the one hand and negative sentiment and deceptiveness on the other.…”

Section: ) Sentiment-aware Systemsmentioning

confidence: 99%

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

et al. 2023

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The growth of the online review phenomenon, which has expanded from specialised trade magazines to end users via online platforms, has also increasingly involved the cultural heritage of countries, a source of tourism and growth driver of local economies. Unfortunately, this has been paralleled by the emergence and spread of the phenomenon of fake reviews, against which the scientific world has developed language models capable of distinguishing them from the truthful. The application of such models, often based on deep neural networks with transformer-type architectures, is however limited by the availability of local language data sets for specific domains, useful for both training and verification. The purpose of this article is twofold. Firstly, a new data set was created in the Italian language, generally considered low-resource, relating to the domain of cultural heritage in Italy, by collecting reviews available online, reorganising them in the form of a data set usable by the language models. Secondly, a baseline of results for the detection of misleading reviews was constructed by exploiting two widely used language models, namely BERT and ELECTRA. The performance achieved is interesting, around 95% accuracy and F1 score, using data set splits between training and testing of 80/20 and 90/10. In addition, SHAP was used as a tool to support the explicability of AI models: in this way, it was possible to show the usefulness of sentiment analysis as a support for the recognition of deceptiveness.INDEX TERMS Italian cultural heritage, data set, fake reviews, sentiment analysis, deceptive.

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“…Simulation experiments have been done on three versions of labeled movie reviews dataset [8] consisting of 1400, 2000, and 10662 movie reviews respectively. Also, in [9], the authors used Naive Bayes, Decision tree, SVM, Random forest and Maximum entropy classifiers in detecting fake reviews on the dataset that they have collected. The collected dataset is around 10,000 negative tweets related to Samsung products and their services.…”

Section: Related Workmentioning

confidence: 99%

Fake Reviews Detection using Supervised Machine Learning

Elmogy¹,

Tariq²,

Mohammed³

et al. 2021

IJACSA

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With the continuous evolve of E-commerce systems, online reviews are mainly considered as a crucial factor for building and maintaining a good reputation. Moreover, they have an effective role in the decision making process for end users. Usually, a positive review for a target object attracts more customers and lead to high increase in sales. Nowadays, deceptive or fake reviews are deliberately written to build virtual reputation and attracting potential customers. Thus, identifying fake reviews is a vivid and ongoing research area. Identifying fake reviews depends not only on the key features of the reviews but also on the behaviors of the reviewers. This paper proposes a machine learning approach to identify fake reviews. In addition to the features extraction process of the reviews, this paper applies several features engineering to extract various behaviors of the reviewers. The paper compares the performance of several experiments done on a real Yelp dataset of restaurants reviews with and without features extracted from users behaviors. In both cases, we compare the performance of several classifiers; KNN, Naive Bayes (NB), SVM, Logistic Regression and Random forest. Also, different language models of n-gram in particular bi-gram and tri-gram are taken into considerations during the evaluations. The results reveal that KNN(K=7) outperforms the rest of classifiers in terms of f-score achieving best f-score 82.40%. The results show that the f-score has increased by 3.80% when taking the extracted reviewers behavioral features into consideration.

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Detecting Negative Deceptive Opinion from Tweets

Cited by 5 publications

References 19 publications

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

A new Italian Cultural Heritage data set: detecting fake reviews with BERT and ELECTRA leveraging the sentiment

Fake Reviews Detection using Supervised Machine Learning

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