Cyberbullying Revelation in Twitter Data Using Naïve Bayes Classifier Algorithm

Kovoor, Binsu C.; Nandakumar, Vandana; Sreeja, M. U.

doi:10.26483/ijarcs.v9i1.5396

Cited by 14 publications

(5 citation statements)

References 12 publications

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“…The plotted graph shows that naïve bayes classifier shows better precision than support vector machine model. We can conclude that for text data classification Naive bayes classifier shows better performance than the SVM model [12]. Again the previous work noted in [11] makes use of Facebook posts to identify and classify cyber bullying.…”

Section: Related Worksupporting

confidence: 54%

“…We can also grasp an idea about if there is any risk in real life that can occur in near future. Because study finds, most of the bullying cases occur for real life hostility [12].…”

Section: E Fuzzy Rule Setmentioning

confidence: 87%

“…VandanaNandakumar et al has done another survey on Twitter data using Naive Bayes classifier algorithm and Support Vector Machine model [12]. The feature probabilities are calculated using Naïve Bayes Classifier Algorithm.…”

Section: Related Workmentioning

confidence: 99%

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

Akhter¹,

Uzzal²,

Polash³

2019

IJMSC

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The advent of different social networking sites has enabled people to easily connect all over the world and share their interests. However, Social Networking Sites are providing opportunities for cyber bullying activities that poses significant threat to physical and mental health of the victims. Social media platforms like Facebook, Twitter, Instagram etc. are vulnerable to cyber bullying and incidents like these are very common now-a-days. A large number of victims may be saved from the impacts of cyber bullying if it can be detected and the criminals are identified. In this work, a machine learning based approach is proposed to detect cyber bullying activities from social network data. Multinomial Naïve Bayes classifier is used to classify the type of bullying.With training, the algorithm classifies cyber bullying as-Shaming, Sexual harassment and Racism. Experimental results show that the accuracy of the classifier for considered data set is 88.76%. Fuzzy rule sets are designed as well to specify the strength of different types of bullying.

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Section: Related Worksupporting

confidence: 54%

“…We can also grasp an idea about if there is any risk in real life that can occur in near future. Because study finds, most of the bullying cases occur for real life hostility [12].…”

Section: E Fuzzy Rule Setmentioning

confidence: 87%

See 1 more Smart Citation

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

Akhter¹,

Uzzal²,

Polash³

2019

IJMSC

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show abstract

“…The major drawback of this paper is the presence of few or no labelled datasets that future researchers can work on instead of gathering new datasets. Nandakumar et al (2018) worked on cyberbullying detection in email application using the Naïve Bayes classification algorithm. The system involved the identification and filtering of spams in emails; then applying the Naïve Bayes classification algorithm to classify the denoised messages.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

Azeez

Misra

LAWAL

et al. 2022

Journal of Cases on Information Technology

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The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.

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“…They collected the Twitter data by using the Twitter API, they used the SVM classifier, and their results are showing an accuracy of 84% . Another group of researchers, Nandakumar et al [33]., have applied the "binary classification" for the purpose of CyberBullying detection in the tweets by using the NB algorithm. They collected the data using Twitter API; after this, they removed the noise from the collected data, applied the NB classifier, and select the feature.…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic CyberBullying Detection Using Arabic Sentiment Analysis

Almutiry

Fattah

2021

The Egyptian Journal of Language Engineering

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The Sentiment Analysis is used for the text analysing, detecting opinion, and classification of the text attitude. It becomes quite challenging when it is applied to the Arabic language due to the structural and morphological complexity, known as "Arabic Sentiment Analysis (ASA)." For the implementation of ASA, we are using the computing advancement in the form of Machine Learning (ML) and Support Vector Machine (SVM) algorithm to train a dataset which is collected automatically through ArabiTools and Twitter API. The dataset contents are labelled by both means, automatic and manual, in order to maintain the efficiency of the detection of CyberBullying tweets. Use internet technology to bully a person by using aggressive and offensive words is known asCyberBullying. The dataset is automatically labelled with respect to the nature of the tweet. If a tweet contains one or more CyberBullying words, it is labelled as CyberBullying, while if there is not any word with aggressive meaning found, it is marked as the NonCyberBullying. After the data collection, there are several pre-processing techniques utilized, including the Normalization, Tokenization, Light Stemmer, ArabicStemmerKhoja, and Term Frequency-Inverse Document Frequency (TF-IDF)" term weighting schema." After the preliminary steps, (SVM), a standard "supervised algorithm," is used with WEKA and Python. There are three experiments that take place one with the WEKA tool using the Light Stemmer, the other is again with WEKA using ArabicStemmerKhoja, and the final experiment was performed with Python. The results are showing the WEKA is more efficient in classifying the text correctly, while Python is more effective with time to build the model. WEKA using the Light Stemmer have the efficiency of 85.49% and taken 352.51 seconds, and the WEKA using ArabicStemmerKhoja have the efficiency of 85.38% and taken 212.12 seconds, while the Python have the efficiency of 84.03% and taken 142.68 seconds.

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Cyberbullying Revelation in Twitter Data Using Naïve Bayes Classifier Algorithm

Cited by 14 publications

References 12 publications

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

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

Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

Arabic CyberBullying Detection Using Arabic Sentiment Analysis

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