Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks

Sentiment analysis is one of the new challenges appeared in automatic language processing with the advent of social networks. Taking advantage of the amount of information is now available, research and industry have sought ways to automatically analyze sentiments and user opinions expressed in social networks. In this paper, we place ourselves in a difficult context, on the sentiments that could thinking of suicide. In particular, we propose to address the lack of terminological resources related to suicide by a method of constructing a vocabulary associated with suicide. We then propose, for a better analysis, to investigate Weka as a tool of data mining based on machine learning algorithms that can extract useful information from Twitter data collected by Twitter4J. Therefore, an algorithm of computing semantic analysis between tweets in training set and tweets in data set based on WordNet is proposed. Experimental results demonstrate that our method based on machine learning algorithms and semantic sentiment analysis can extract predictions of suicidal ideation using Twitter Data. In addition, this work verify the effectiveness of performance in term of accuracy and precision on semantic sentiment analysis that could thinking of suicide.