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

B Sumathy,D Sungeetha,Anand Kumar,Stephen Jeswinde Nuagah,Ankur Saxena,Arshad Hashmi,Piyush Kumar Shukla

doi:10.1155/2022/5906797

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.

Highlights

Analysis of the emotions is used to find user feelings or opinions. An individual has his own space in social media, such as Twitter, to post an idea or topic or comment on a service. e user review shows that various models of sentiment analysis in natural languages have been developed, film reviews, product reviews, political reviews, and so forth, for feelings analysis
Nonnative English speakers have been highly influenced by social media such as Twitter. ere are different discourse challenges for nonnative English speakers when expressing an opinion on social media. e first challenge is to develop grammar rules for classifying feelings in Tamil tweets. e second problem is that there are insufficient resources, such as dataset and feeling lexicons. e last question is to improve slang words’ performance, words transliterated in various languages and fields
This paper proposes the principal component of the sentiment analysis scheme. e proposed regulations on language grammar for Tamil tweets’ classification are a characteristic feature by which user feelings are identified, and tweets are grouped into a set of categories. e work proposed contributes to new grammar rule-based algorithms for the Tamil tweets

Summary

Methods

Analysis of predicted class Figure 1: Sentiment analysis framework for Tamil movie tweets. Imperfect, and luminous information, the preprocessing of data is done. E first job is to delete URLs. Usually, the Uniform Resource Locator does not help in informal words to assess the feeling. A technique for removing the Uniform Resource Locator is used to avoid such errors. E following task is to remove retweets. Retweeting is the process of copying a tweet and posting it to a second user. Is is usually if a user likes another user’s tweets. Tokenization is a way of dividing words into different words or tokens into user tweets. A phrase, word, or symbol might be a token. E tweet phrases are tokened into a series of words that can be analyzed with white spaces to remove any specific character or punctuation marks such as # and @. A phrase, word, or symbol might be a token. e tweet phrases are tokened into a series of words that can be analyzed with white spaces to remove any specific character or punctuation marks such as # and @. e various Documentary Dictionaries are called token sets produced by combining the full text of a collection

Results

Experimental Results

Method