LDA Model

So, when using the LDA model to model the whole text set, the number of topics in the LDA model will influence the performance of modeling greatly [82]. Therefore, how to determine the optimal number of topics in the LDA model is another research hotspot of LDA.

At present, to determine the optimal number of topics in the LDA model has two main methods: the selection method based on Hierarchical Dirichlet Process (HDP) [86] [87] and the standard method in Bayesian statistics [82]. The former uses the nonparametric feature of Dirichlet Process (DP) to solve the selection problem of the optimal number of topics in LDA. But, it needs to establish a HDP model and a LDA model respectively for the same one data set. Obviously, when processing practical

The aim is to illustrate the dynamics in communication and relationships introduced with the emergence of Internet in everyday life. We show that virtual environments represent and act as a society of which participants demonstrate behavior that is similar to what can be observed in real-life society, and argue that as a consequence the interventions and precautions toward social misbehaviors such as cyberbullying should be similar to the ones that are known to be effective in real-life societies.

Objective 2: To make a complete dataset from Social Network to study cyber bullying.

One of the main challenges that were faced during this research was lack of suitable and available dataset for research into cyber bullying detection and into digital tools that could contribute to its prevention. The required dataset has to contain a balanced number of bullying and non-bullying comments from Twitter users. It should include certain types of metadata, such as demographic information for the authors of posts, as well as details on the history of their network

Most reviews depend on text mining ideal models that recognize social conduct molded through online dialect, for example, distinguishing on the web sexual stalkers and pedophiles, identification of dangerous article updates, purported vandalism recognition , spam identification, recognition of web manhandle and digital fear mongering and last however not slightest the reviews led on hostile dialect use in online networking. In our text mining approach of text mining approach of detecting bullying words, we utilized the information about the author in order to improve the accuracy of bullying comments.

Objective 4: To Evaluate the performance of the proposed system.

The performance of the system is calculated using the precision, recall and F-1 measure based on the top ranked features generated through B-LDA method against the truth set as tested on the datasets. The overall classification accuracy of the system is the best performance during different experiments.

Objective 5: To identify the potential predators using Graph