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Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System
The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, e...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898115/ https://www.ncbi.nlm.nih.gov/pubmed/35256878 http://dx.doi.org/10.1155/2022/5906797 |
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author | Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde |
author_facet | Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde |
author_sort | Sumathy, B. |
collection | PubMed |
description | The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. |
format | Online Article Text |
id | pubmed-8898115 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88981152022-03-06 Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Comput Intell Neurosci Research Article The emergence of social media has allowed people to express their feelings on products, services, films, and so on. The feeling is the user's view or attitude towards any topic, object, event, or service. Overall, feelings have always influenced people's decision-making. In recent years, emotions have been analyzed intensively in natural language, but many problems still have to be watched. One of the most important problems is the lack of precise classification resources. Most of the research into feeling gradation is concerned with the issue of polarity grading, although, in many practical applications, this relatively grounded feeling measure is insufficient. Design methods are therefore essential, which can accurately classify feelings into a natural language. The principal goal of the research is to develop an overflow of grammatical rules-based classification of Indian language tweets. In this work, three main challenges are identified to classify feelings in Indian language tweets and possible methods for tackling such issues. Firstly, it has been found that the informal nature of tweets is crucial for the classification of feelings. Based on the tweets, the mental illness of the person has been classified. Therefore, to categorize Indian language tweets, a combination of grammar rules based on adjectives and negations is proposed. Secondly, people often express their feelings with slang words, abbreviations, and mixed words. A technique called field tags is used to include nongrammatical arguments such as slang words and diverse words. Thirdly, if a tweet is more complex, the morphological richness of the Indian language results in a loss of performance. The grammar rules are embedded in N-gram techniques and machine learning methods. These methods are grouped into three approaches, which functionally predict Indian language tweets with syntactic words. Hindawi 2022-02-26 /pmc/articles/PMC8898115/ /pubmed/35256878 http://dx.doi.org/10.1155/2022/5906797 Text en Copyright © 2022 B. Sumathy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Sumathy, B. Kumar, Anand Sungeetha, D. Hashmi, Arshad Saxena, Ankur Kumar Shukla, Piyush Nuagah, Stephen Jeswinde Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title | Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title_full | Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title_fullStr | Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title_full_unstemmed | Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title_short | Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System |
title_sort | machine learning technique to detect and classify mental illness on social media using lexicon-based recommender system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8898115/ https://www.ncbi.nlm.nih.gov/pubmed/35256878 http://dx.doi.org/10.1155/2022/5906797 |
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