Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

Abstract

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.

Elevating Offensive Language Detection: CNN-GRU and BERT for Enhanced Hate Speech Identification

“…Including broader offensive terms like "trash" or "swine" might enhance recall at the expense of precision. Researchers have explored various algorithms, from early lexicon-based methods to modern Neural Network techniques [5], to identify hate speech and offensive content. However, algorithm performance can vary significantly depending on the dataset, making it challenging to conclude that a specific method universally excels across all datasets [6].…”

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

“…Several studies have been undertaken to improve public health systems by leveraging social media health-related data, machine or deep learning models, and Natural Language Processing (NLP) techniques, such as text mining and sentiment analysis. These studies include the detection of various diseases through social networks, such as COVID-19 [13][14][15], latent infectious diseases [16], infectious diseases [17], depression [18][19][20], mental illness [21,22], mosquito-borne diseases [23], Asperger syndrome [24], dengue disease [25], avian influenza [26], and influenza [27][28][29][30][31], among others.…”

Retracted: Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender System

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”