The complexity of criminal minded' experiences reflected from social media content requires human interpretation. However, the growing scale of data demands automatic data analysis techniques. In this project data mining algorithm based on Naïve Bayes Multi-Label Classifier is implemented which contains several steps like Data Collection from twitter, Cleaning the data by removing stop words, removal of non letter and punctuation marks, probability of the words for various categories namely

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... y Study Load, Sleep Problems, Lack of Social Engagement, Negative Emotion and Diversity Issues is estimated. For all the tweets Accuracy, Precision, Recall, F1 measure, Micro Averaged & Macro Averaged values are computed for each category and also for the various users. Therefore we can conclude on average how many criminal minded have various categories of problems as well as extend this to the problems faced by which user. Keywords-Cyber analytics, data mining, machine learning INTRODUCTION I. INTRODUCTION On Tweeter social media site, criminal minded discuss and share their everyday encounters in an informal and casual manner. Criminal minded' digital footprints provide vast amount of implicit knowledge and a whole new perspective for educational researchers and practitioners to understand criminal minded' experiences outside the controlled classroom environment. This understanding can inform institutional decision-making on interventions for at-risk criminal minded, improvement of education quality, and thus enhance student recruitment, retention, and success . The large amount of social media data provides opportunities to understand criminal minded' experiences, but also raises methodological difficulties in making sense of social media data for educational purposes. Just imagine the sheer data volumes, the diversity of Internet slangs, the unpredictability of locations, and timing of criminal minded posting on the web, as well as the complexity of criminal minded' experiences. Pure manual analysis cannot deal with the ever growing scale of data, while pure automatic algorithms usually cannot capture in-depth meaning within the data. There is huge amount of data available in Information Industry. This data is of no use until converted into useful information. Analysing this huge amount of data and extracting useful information from it is necessary. The extraction of information is not the only process we need to perform; it also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation. Once all these processes are over, we are now position to use this information in many applications such as Fraud Detection, Market Analysis, Production Control, Science Exploration etc. II. STATEMENT OF THE PROBLEM In today's world lot of focus is on the study habits and study processes which improve the knowledge of the criminal minded which are really good. But there are no approaches which mainly concentrate on the mental health of criminal minded. Of course having a specialized consultant at a college decreases the mental by a certain level. Criminal minded are like lovely flowers which have to be always happy and enjoy their life along with concentrating on the career prospects. Also with the advent of social media applications like face book, twitter etc have lot of sentiments placed by