Word Embedding Based Multinomial Naive Bayes Algorithm for Spam Filtering

Kadam, Sumedh; Gala, Aayush; Gehlot, Pritesh; Kurup, Aditya; Ghag, Kranti

doi:10.1109/iccubea.2018.8697601

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…There are many models of NB e-mail filters [26], but we examined 3 models: Multinomial Naïve Bayes algorithm (MNB), Complement Naïve Bayes algorithm (CNB), and Bernoulli Naïve Bayes algorithm (BNB). MNB models [27] are mainly suitable to deal with discrete features. It considers the relationship between word frequencies in the text and the category of the text.…”

Section: Naïve Bayes Filtersmentioning

confidence: 99%

Detecting attacks on e-mail

Fang,

Mogos

2024

IJEECS

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E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Na¨ıve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored.

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Section: Naïve Bayes Filtersmentioning

confidence: 99%

Detecting attacks on e-mail

Fang,

Mogos

2024

IJEECS

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“…By calculating the likelihood of each class given a set of input features, MNB efficiently assigns probabilities to different categories, thus facilitating accurate classification. Despite its simplicity, MNB has demonstrated robust performance in various natural language processing applications, including sentiment analysis [16] and spam detection [56,57].…”

Section: Multinomial Naive Bayesmentioning

confidence: 99%

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Mahmud,

Ptaszynski,

Masui

2024

Electronics

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Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.

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“…This model gets input from a collection of texts and generates a vector of the words. This vector can be used to find the proximity of each word in the vector space [11]. Thus, this model can check all the representation that has been learned and displays the closest word [12], as shown in Table I.…”

Section: Word2vecmentioning

confidence: 99%

Cyberbullying Sentiment Analysis with Word2Vec and One-Against-All Support Vector Machine

Halim

Suryadibrata

2021

International Journal of New Media Technology

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Depression and social anxiety are the two main negative impacts of cyberbullying. Unfortunately, a survey conducted by UNICEF on 3rd September 2019 showed that 1 in 3 young people in 30 countries had been victims of cyberbullying. Sentiment analysis research will be conducted to detect a comment that contains cyberbullying. Dataset of cyberbullying is obtained from the Kaggle website, named, Toxic Comment Classification Challenge. The pre-processing process consists of 4 stages, namely comment generalization (convert text into lowercase and remove punctuation), tokenization, stop words removal, and lemmatization. Word Embedding will be used to conduct sentiment analysis by implementing Word2Vec. After that, One-Against-All (OAA) method with the Support Vector Machine (SVM) model will be used to make predictions in the form of multi labelling. The SVM model will go through a hyperparameter tuning process using Randomized Search CV. Then, evaluation will be carried out using Micro Averaged F1 Score to assess the prediction accuracy and Hamming Loss to assess the numbers of pairs of sample and label that are incorrectly classified. Implementation result of Word2Vec and OAA SVM model provide the best result for the data undergoing the process of pre-processing using comment generalization, tokenization, stop words removal, and lemmatization which is stored into 100 features in Word2Vec model. Micro Averaged F1 and Hamming Loss percentage that is produced by the tuned model is 83.40% and 15.13% respectively. Index Terms— Sentiment Analysis; Word Embedding; Word2Vec; One-Against-All; Support Vector Machine; Toxic Comment Classification Challenge; Multi Labelling

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Word Embedding Based Multinomial Naive Bayes Algorithm for Spam Filtering

Cited by 6 publications

References 4 publications

Detecting attacks on e-mail

Detecting attacks on e-mail

Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Cyberbullying Sentiment Analysis with Word2Vec and One-Against-All Support Vector Machine

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