Cyber Bullying Detection and Classification using Multinomial Naïve Bayes and Fuzzy Logic

Akhter, Arnisha; Uzzal, K. Acharjee; Polash, Md. Masbaul Alam

doi:10.5815/ijmsc.2019.04.01

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…The proposed hybrid approach involves selecting the unigrams with multinomial naïve bayes classifier for classification. The accuracy of the processed approach is shown in figure (2). The confusion matrix of the classifier shows that the sentiment is classified correctly as positive/negative.…”

Section: Resultsmentioning

confidence: 99%

“…Fuzzy logic is used to determine the strength of bully. The data set used is collected from the approach used by Akhter, Arnisha from facebook data [2]. It is shown that naïve bayes classifier has more accuracy than support vector machine model (SVM) and less run time.…”

Section: Literature Surveymentioning

confidence: 99%

“…It is shown that naïve bayes classifier has more accuracy than support vector machine model (SVM) and less run time. The accuracy for the SVM model is 76.38% and for naïve bayes is 88.89% [2]. Concept maps (CM) isanother approach that gives relationship among concepts.…”

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Text Mining to Sentiment Analysis of Online Reviews

Asritha¹,

Reddy²,

Sudha³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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In the prevailing days social networking sites helps people to connect easily across the world and gain knowledge and also share their interests. But, unfortunately in some cases these sites became a platform for cyber bullying. Cyber bullying is the act which causes emotional and psychological distress leading to depression, anxiety, fear and low selfesteem to the victims. Cyberbullying can be elucidated as usage of digital communication typically by sending messages to threaten, defame, harass or intimidate someone. Common social media platforms like twitter, facebook, instagram are exposed to cyberbullying which has become very common now-a-days. This can be reduced to an extent if such intimidating messages or comments are segregated. The process of classifying a sentence whether it is positive, negative or neutral is known as sentiment analysis. It helps in determining emotional tone behind a sentence. To classify these intimidating messages this paper proposes a hybrid classifier approach which classifies reviews into positive or negative. Experimental results show that the accuracy of the classifier for considered dataset is 89.36%.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Text Mining to Sentiment Analysis of Online Reviews

Asritha¹,

Reddy²,

Sudha³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

View full text Add to dashboard Cite

show abstract

“…According to Zaib et al [16], users may exhibit varied and unpredictable behaviors on social media, and therefore, the best approach to track those behaviors is social media mining and behavior analytics. Akther et al [17] suggested that cyberbullying should also be evaluated in relation to terror messages and the study of Akther et al [17] presented a training algorithm that classifies the content of cyberbullying into three sub-categories of harassment, racism, and shaming.…”

Section: Problem Statementmentioning

confidence: 99%

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

AL-Harbi¹,

Kamsin²

2021

IJITCS

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Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.

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“…Almost all of them are not realized, if there is a possibility or a dangerous action will occur. His concern is that social media is a place and target for finding someone who will be the target of crime, one of which is cyberbullying [3]. Negative actions in cyberspace such as insulting, harassing, ridiculing and even threatening the victim.…”

Section: Introductionmentioning

confidence: 99%

Cyberbullying Prediction As Cyber Counseling Tools With Data Mining Classification

Pamuji¹,

Setiawan

2022

Bit

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Pertumbuhan data dan informasi semakin pesat sedangkan pengguna perangkat teknologi informasi terus meningkat. Apalagi kemudahan akses informasi didukung dengan hadirnya teknologi komunikasi bergerak yang dimiliki oleh hampir setiap pengguna. Saat ini terjadi peningkatan jumlah pengguna yang signifikan, yang dinilai berpeluang untuk hadirnya kejahatan dunia maya, terutama dalam kasus bullying. Masalah utama adalah sulitnya memprediksi potensi cyberbullying karena dunia maya penuh dengan anonimitas. Kasus cyberbullying termasuk kejahatan dunia maya di luar keamanan komputer jika dilihat dari perspektif perilaku. Oleh karena itu, cyberbullying menganalisis perilaku ketika melakukan tindakan negatif. Dalam studi ini, kami telah menyelidiki dan memprediksi kecenderungan bullying menggunakan pendekatan data mining. Analisis studi kasus yang disajikan dan teknis pelaksanaannya melalui pendekatan data mining. Menggunakan data mining sebagai alat, kami menggunakan beberapa metode klasifikasi dengan tujuan perbandingan dan metode mana yang terbaik untuk analisis. Metode klasifikasi dalam data mining adalah K-NN, Random Forest, Decision Tree, dan Naive Bayes. Dengan perbandingan dari empat metode, ada tiga kelas. Ada tiga kelas, yaitu tidak ada potensi bullying, kekerasan dan hinaan. Dengan demikian, tiga kelas terdeteksi berdasarkan hasil investigasi dan beberapa teknik dievaluasi dengan menggunakan perbandingan kinerja masing-masing teknik. Hasil akhir akan menunjukkan teknik decision tree berkinerja terbaik dikarenakan selama tahap preprocessing tidak membutuhkan waktu lama. tambahannya adalah karena kemampuan membreakdown setiap cabang dari data sehingga waktu yang dibutuhkan pada data cleaning lebih cepat dengan akselerasi 7%.

show abstract

Cyber Bullying Detection and Classification using Multinomial Naïve Bayes and Fuzzy Logic

Cited by 12 publications

References 8 publications

Intelligent Text Mining to Sentiment Analysis of Online Reviews

Intelligent Text Mining to Sentiment Analysis of Online Reviews

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

Cyberbullying Prediction As Cyber Counseling Tools With Data Mining Classification

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