The growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. The large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. People's feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier.

This paper describes an implementation for group profiling assisting language learning in social networking sites and particularly in Facebook. In this study, we have developed an educational application in Facebook for grammar learning, while the whole procedure is assisted by group profiling. We applied K-means clustering algorithm to determine the groups of students with the same learning styles and abilities, creating a more sophisticated and dynamic model. The algorithm takes as input, to initialize the process, two important studentsâ?? characteristics â?? age and knowledge. Our study exploits the fact that educational systems have a large number of users and we use a machine learning reasoning mechanism that is based on recognized similarities between them. Future plans include evaluating our Facebook application in order to examine the degree of usefulness of the group profiling for language learning.

The prevalence of mental health problems has become alarming, with the worldwide occurrence of mental health problems and deaths due to suicide reaching significant numbers. In the Philippines, suicide rate has increased from 1984 to 2005 especially among the youth. This is evident in campus setting in the Philippines, with the death of at least ten college students in the past three years. Despite this prevalence, many of those experiencing mental health problems do not seek help because of factors such as inaccessibility of help and the stigma and discrimination brought by mental health conditions. Using insights collected from a series of consultations with the campus' counseling center, administrators, faculty, and students of the University of the Philippines Manila, this project designed and developed a mobile application which will provide a more convenient and accessible mental health first aid service to students that helps them avoid stigma and discrimination. The application connects students with trained peer counselors, while at the same time, protecting their anonymity through safeguards. It also collects data regarding the frequency of the software's use and the demographics of active users, which can be used in understanding the mental health status of each campus. The project shows potential not only in providing mental health intervention, but also in studying and preventing mental illnesses in college campuses.

Facebook applications are more convenient and flexible to use, since students tend to invest a good deal of time in the use of such technologies, given that Facebook retains the educational quality, as it is widely used in instructional contexts. To this direction, our Facebook application promotes language learning. Furthermore, it incorporates machine learning techniques in order to offer user classification based on multiple user characteristics. Our resulting Facebook language learning application has been evaluated by both instructors and Facebook users. The results of the evaluation showed that Facebook provides opportunities within curriculum education and learning and user classification in such applications promotes the cognitive procedure.

This paper describes the mining of relationships among user clusters in Facebook for tutoring languages. In this study, we have visualized the Facebook user characteristics used in classification procedure. We applied K-means clustering algorithm to determine the groups of users with the same learning styles and capabilities. The aforementioned algorithm groups them by taking as input, to initialize the process, several fundamental user characteristics. Our study exploits the fact that tutoring systems have a large number of users and we use a machine learning reasoning mechanism, which is based on recognized similarities between them. The overall goal of this data mining process is to extract information from the user data set and transform it into an understandable structure for further use. Future plans include deeper study on the relationship between the different Facebook characteristics and clarifying which characteristic has the strongest effect on the clustering procedure.