Fake Review Classification Using Supervised Machine Learning

Khan, Hanif; Asghar, Muhammad Usama; Asghar, Muhammad Zubair; Srivastava, Gautam; Maddikunta, Praveen Kumar Reddy; Gadekallu, Thippa Reddy

doi:10.1007/978-3-030-68799-1_19

Cited by 17 publications

(4 citation statements)

References 31 publications

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“…WordCloud is used to visualize a massive amount of data with the use of a sentiment analysis lexicon [25]. Machine learning techniques can be extended to classify fake reviews, fake news, aspect analysis, and DNA sequence mining [16,26,27,28]. e text classification is improved using the two-stage text feature selection algorithm [29,30].…”

Section: Related Workmentioning

confidence: 99%

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Rodrigues¹,

Fernandes²,

Aakash³

et al. 2022

Computational Intelligence and Neuroscience

119

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In this modern world, we are accustomed to a constant stream of data. Major social media sites like Twitter, Facebook, or Quora face a huge dilemma as a lot of these sites fall victim to spam accounts. These accounts are made to trap unsuspecting genuine users by making them click on malicious links or keep posting redundant posts by using bots. This can greatly impact the experiences that users have on these sites. A lot of time and research has gone into effective ways to detect these forms of spam. Performing sentiment analysis on these posts can help us in solving this problem effectively. The main purpose of this proposed work is to develop a system that can determine whether a tweet is “spam” or “ham” and evaluate the emotion of the tweet. The extracted features after preprocessing the tweets are classified using various classifiers, namely, decision tree, logistic regression, multinomial naïve Bayes, support vector machine, random forest, and Bernoulli naïve Bayes for spam detection. The stochastic gradient descent, support vector machine, logistic regression, random forest, naïve Bayes, and deep learning methods, namely, simple recurrent neural network (RNN) model, long short-term memory (LSTM) model, bidirectional long short-term memory (BiLSTM) model, and 1D convolutional neural network (CNN) model are used for sentiment analysis. The performance of each classifier is analyzed. The classification results showed that the features extracted from the tweets can be satisfactorily used to identify if a certain tweet is spam or not and create a learning model that will associate tweets with a particular sentiment.

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Section: Related Workmentioning

confidence: 99%

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Rodrigues¹,

Fernandes²,

Aakash³

et al. 2022

Computational Intelligence and Neuroscience

119

View full text Add to dashboard Cite

show abstract

“…Furthermore, a comparative analysis of various machine learning methods is presented [ 6 , 7 ]. In [ 8 ], the authors proposed different supervised machine learning solutions to detect fake businesses. Moreover, they also tested over 300,000 accounts using random forest and XGBoost classifiers.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Ashfaq

Khalid

Yahaya

et al. 2022

Sensors

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In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

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“…The dependencies found to play a significant role in producing scores and need to be confirmed from an expert in domain [12], [13]. These techniques involve using natural language tools to parse the text and find its semantic meaning [14], [15]. That meaning can then be compared with the meaning obtained from the solution to assign the final score.…”

Section: ) Information Extraction (Ie) Techniquementioning

confidence: 99%

Subjective Answers Evaluation Using Machine Learning and Natural Language Processing

Bashir

Arshad

Javed³

et al. 2021

IEEE Access

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Subjective paper evaluation is a tricky and tiresome task to do by manual labor. Insufficient understanding and acceptance of data are crucial challenges while analyzing subjective papers using Artificial Intelligence (AI). Several attempts have been made to score students' answers using computer science. However, most of the work uses traditional counts or specific words to achieve this task. Furthermore, there is a lack of curated data sets as well. This paper proposes a novel approach that utilizes various machine learning, natural language processing techniques, and tools such as Wordnet, Word2vec, word mover's distance (WMD), cosine similarity, multinomial naive bayes (MNB), and term frequencyinverse document frequency (TF-IDF) to evaluate descriptive answers automatically. Solution statements and keywords are used to evaluate answers, and a machine learning model is trained to predict the grades of answers. Results show that WMD performs better than cosine similarity overall. With enough training, the machine learning model could be used as a standalone as well. Experimentation produces an accuracy of 88% without the MNB model. The error rate is further reduced by 1.3% using MNB.

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Fake Review Classification Using Supervised Machine Learning

Cited by 17 publications

References 31 publications

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

Real-Time Twitter Spam Detection and Sentiment Analysis using Machine Learning and Deep Learning Techniques

A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

Subjective Answers Evaluation Using Machine Learning and Natural Language Processing

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