Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis

Ezpeleta, Enaitz; Garitano, Iñaki; Zurutuza, Urko; Hidalgo, José María Gómez

doi:10.1142/s0218488517400177

Cited by 10 publications

(8 citation statements)

References 16 publications

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“…And the new proposed token features and new topic features have better performance of 97.36% and 98.10% than the same type features in [17] which are 94.69% and 97.99%. Enaitz et al [38] combine personality recognition and sentiment analysis techniques to analyze Short Message Services (SMS) texts and the best result reaches to a 99.01% which is as same as the best result in this paper. By comparison, the method proposed in this paper is effective and well performing.…”

Section: Experiments and Results Analysissupporting

confidence: 75%

See 1 more Smart Citation

SMS Phishing Detection Using Oversampling and Feature Optimization Method

Wu¹,

Zheng²,

Wu³

et al. 2018

dtcse

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In this paper, a new SMS phishing detection method using oversampling and feature optimization technology is proposed to improve SMS phishing detection accuracy. Three types features are presented including token features, topic features and Linguistic Inquiry and Word Count (LIWC) features. One of the existing oversampling methods called Adaptive Synthetic Sampling Approach is applied in this paper since it has good performance. Then, Binary Particle Swarm Optimization(BPSO) algorithm is used to analyze the three types features and select the optimal combination of all the features. Finally, the detection results are achieved by Random Forest classification algorithm. Experimental results show oversampling method and feature optimization method improve the accuracy of SMS phishing detection. The best accuracy of the proposed method is 99.01% with an average of 86.6 features. The results demonstrate that the proposed method has a promising performance for SMS phishing detection.

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Section: Experiments and Results Analysissupporting

confidence: 75%

“…Results Details [16] 97.64% With 30% samples for training [17] 99.21% Using 480 features [38] 99.01% Combine personality recognition and sentiment analysis…”

Section: Referencementioning

confidence: 99%

SMS Phishing Detection Using Oversampling and Feature Optimization Method

Wu¹,

Zheng²,

Wu³

et al. 2018

dtcse

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show abstract

“…For example, take the following two SMS messages from the UCI repository dataset, which can be downloaded from http://www.dt.fee.unicamp.br/~tiago/smsspamcollection. The same dataset has been used in [2,4,[8][9][10][11]13,21,23,25,31,46,[52][53][54][55][56][57] for performance evaluations.…”

Section: Observation States and Observation Sequencementioning

confidence: 99%

“…SMS traffic volumes have risen from 1.46 billion in 2000 to 7.9 trillion in 2012 [1]. SMS-capable mobile phone users had reached 6.1 billion users by the year 2015 [2]. The growth of mobile users has generated a great deal of revenue [1].…”

Section: Introductionmentioning

confidence: 99%

A Discrete Hidden Markov Model for SMS Spam Detection

Xia

Chen

2020

Applied Sciences

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

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“…Various researches have been done related to spam detection and filter as we can see in the works of Nagwani and Sharaff [5], Sah and Parmar [6], Bhuiyan et al [7], Ezpeleta et al [8], and Jawale et al [9]. However, the most used method to prevent spam is a text mining method with Bayesian filtering.…”

Section: Introductionmentioning

confidence: 99%

Indonesian language email spam detection using N-gram and Naïve Bayes algorithm

Vernanda¹,

Hansun²,

Kristanda³

2020

Bulletin EEI

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Indonesia is ranked the top 8th out of the total country population in the world for the global spammers. Web-based spam filter service with the REST API type can be used to detect email spam in the Indonesian language on the email server or various types of email server applications. With REST API, then there will be data exchange between the applications with JSON data type using existing HTTP commands. One type of spam filter commonly used is Bayesian Filtering, where the Naïve Bayes algorithm is used as a classification algorithm. Meanwhile, the N-gram method is used to increase the accuracy of the implementation of the Naïve Bayes algorithm in this study. N-gram and Naïve Bayes algorithms to detect spam email in the Indonesian language have successfully been implemented with accuracy around 0.615 until 0.94, precision at 0.566 until 0.924, recall at 0.96 until 1.00, and F-measure at 0.721 until 0.942. The best solution is found by using the 5-gram method with the highest score of accuracy at 0.94, precision at 0.924, recall at 0.96, and F-measure value at 0.942.

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Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis

Cited by 10 publications

References 16 publications

SMS Phishing Detection Using Oversampling and Feature Optimization Method

SMS Phishing Detection Using Oversampling and Feature Optimization Method

A Discrete Hidden Markov Model for SMS Spam Detection

Indonesian language email spam detection using N-gram and Naïve Bayes algorithm

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