Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

Hadj Ahmed Bouarara
Copyright: © 2022 |Pages: 15
DOI: 10.4018/978-1-6684-6303-1.ch032
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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' behavior in social media in order to help Twitter users in overcoming their mental health problems such as anxiety, phobia, depression, paranoia, etc. For this, the author used text mining and machine learning algorithms (naïve Bayes, k-nearest neighbours) to analyse tweets. The obtained results were validated using different evaluation measures such as f-measure, recall, precision, entropy, etc.
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Literature Review (Related Work)

The work of Hatzivassiloglou and McKeown in 1997 (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 (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.

In the same year, researchers Yu and Hatzivassiloglou (2003) used the probability of ranking a word to measure the strength of the orientation of the named entities. In 2006, researchers Kanayama and Nasukawa (2006) as well as Ding and Liu (2008) in 2008 proposed, for their part, a learning-based approach that uses the coordination conjunctions present between a word already classified and a word unclassified.

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