Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning

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.