Paper Info
Title
Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks.
Abstract
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.
Year
DOI
Venue
2017
10.1016/j.procs.2017.08.290
Procedia Computer Science
Keywords
Field
DocType
Sentiment Analysis,Machine Learning,Suicide,Social Networks,Tweets,Semantic Sentiment Analysis
Training set,Data mining,Social network,Computer science,Sentiment analysis,Algorithm,Suicidal ideation,Artificial intelligence,WordNet,Vocabulary,Machine learning
Conference
Volume
ISSN
Citations 
113
1877-0509
4
PageRank 
References 
Authors
0.44
8
3
Name
Order
Citations
PageRank
Marouane Birjali1143.57
Abderrahim Beni Hssane26314.14
Mohammed Erritali31410.03