Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media

Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media

Hadj Ahmed Bouarara
DOI: 10.4018/IJSSCI.2021070101
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Abstract

A recent British study of people between the ages of 14 and 35 has shown that social media has a negative impact on mental health. The purpose of the paper is to detect people with mental disorders' behaviour in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia. The authors have adapted the recurrent neural network (RNN) in order to prevent the situations of threats, suicide, loneliness, or any other form of psychological problem through the analysis of tweets. The obtained results were validated by different experimental measures such as f-measure, recall, precision, entropy, accuracy. The RNN gives best results with 85% of accuracy compared to other techniques in literature such as social cockroaches, decision tree, and naïve Bayes.
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2. Literature Review (Related Work)

Microblogging websites have evolved to become a source of varied kind of information. This is due to nature of microblogs on which people post real time messages about their opinions on a variety of topics, discuss current issues, complain, and express positive sentiment for products they use in daily life. In fact, companies manufacturing such products have started to poll these microblogs to get a sense of general sentiment for their product.

The work of Hatzivassiloglou and McKeown in 1997 (Hatzivassiloglou, 1997) consists in using the coordinating conjunctions present between a word already classified and an unclassified word, followed by the contributions of researcher Nasukawa and his team in 2003 (Nasukawa, 2003) who proposed a new method for extracting associated concepts from segments and summing the orientations of the opinion vocabulary present in the same segment.

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