An automated Psychometric Analyzer based on Sentiment Analysis and Emotion Recognition for healthcare

Vij, Anneketh; Pruthi, Jyotika

doi:10.1016/j.procs.2018.05.033

Cited by 23 publications

(7 citation statements)

References 20 publications

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“…Interdisciplinary approaches to researching various issues are becoming popular nowadays (Ruben, 2018). Following modern trends of research, the current study integrated disciplines of computational linguistics (Poecze, Ebster, & Strauss, 2018;Sharma et al, 2018), corpus linguistics (Bhatia, Garg, & Johari, 2018) and psychology (Vij & Pruthi, 2018) to analyse features of written discourse.…”

Section: Methodsmentioning

confidence: 99%

“…Not just computational linguists (Asghar, Khan, Khan, & Kundi, 2018), but psychologists (Vij & Pruthi, 2018), neurologists, and practitioners in the field of medicine (Bulmer, Bӧhnke, & Lewis 2017) are also interested in conducting sentiment analysis; they adopt and adapt different methods for sentiment analysis according to their requirements and some of these methods are discussed below.…”

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confidence: 99%

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Sentiment Analysis of Imran Khan’s Tweets

Saeed¹,

Zahra²,

Fayyaz³

2021

PJPR

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In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Sentiment Analysis of Imran Khan’s Tweets

Saeed¹,

Zahra²,

Fayyaz³

2021

PJPR

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“…The authors want to eventually compile a sizable user base with more precise self-reported sadness signals. A sentiment analysis and emotion recognition-based automated psychometric analyzer for healthcare has been looked at by Vij [4]. They asserted and came to the conclusion that the main objective of the proposed work is to develop a self-service medical kiosk or a psychometric analyzer with fast computational linguistics capabilities that can produce a brief, concise summary of the patient's emotional health based on previous records, medications, and treatments.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Language Patterns for Depression Detection on Social Media: Insights from Reddit Data and Machine Learning Techniques

Ayyalasomayajula

2024

Preprint

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Language can disclose a lot about a person’s emotional condition, social standing, and even personality. In the proposed research, the authors looked at the words that are most helpful in determining if a person is depressed or not. Additionally, the authors tried to include a variety of dataset features to see if they could help us form a more accurate judgement. The authors obtained the data from the social networking site Reddit. It includes erratic messages and comments made by a group of depressed people. Given that it performed the best, the authors used logistic regression using the Term Frequency-Inverse Document Frequency (TFIDF) for the proposed model. One of the factors that contributed to a marginal speed improvement was the average time between two consecutive postings or comments. In F1-score, the proposed model fared better than other models utilising the same dataset. The diagnosis of mental disease is turning into a significant issue as people’s understanding of the value of mental health grows. Many psychiatrists found it challenging to make a diagnosis of mental disease in a patient due to the complexity of each mental disorder, making it impossible to start the patient on the right course of treatment before it was too late. However, the significance of integrating into people’s daily life has produced a setting in which further knowledge about a patient’s mental illness may be acquired.

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“…As a result, rigorous research is taking place in this direction for proper psychometric analysis of people in order to understand their psychological problems like bipolar disorders, severe depression, anxiety and schizophrenia etc. It also carries a vital significance in analyzing the emotional health of patients which aids the treatment process (Vij & Pruthi, 2018 ) (Mohanty, Pratihar, & Suar, 2015 ). Vij and Pruthi ( 2018 ) have developed a psychometric analyzer that analyzes the emotional health of patients based on medical history and recorded communication .…”

Section: Emotional Health Analysismentioning

confidence: 99%

“…It also carries a vital significance in analyzing the emotional health of patients which aids the treatment process (Vij & Pruthi, 2018 ) (Mohanty, Pratihar, & Suar, 2015 ). Vij and Pruthi ( 2018 ) have developed a psychometric analyzer that analyzes the emotional health of patients based on medical history and recorded communication . Here, the emotional health is analyzed in terms of various components viz.…”

Section: Emotional Health Analysismentioning

confidence: 99%

Employing Machine Learning for Multi-perspective Emotional Health Analysis

Mangla

Akhare

Deokar

et al. 2020

Emotion and Information Processing

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Human emotion is undoubtedly the most fundamental aspect of human relationships and thus guides human to distinguish among right and wrong human behavior. Also, emotional equilibrium governs the human happiness state which indirectly affects physical health. In the absence of affective emotional equilibrium, unhappiness and common mental disorders may emerge. Despite the powerful influence of emotion on human behavior, it lacks systematic research and has been confined to fixation of beliefs only. This lack of recognition shows an ignorant attitude toward emotional health leading to unhappiness among humans. Here, in this chapter, the authors aim to present the importance of emotional health analysis. Additionally, they discuss the various perspective in order to evaluate the emotional health of humans.

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An automated Psychometric Analyzer based on Sentiment Analysis and Emotion Recognition for healthcare

Cited by 23 publications

References 20 publications

Sentiment Analysis of Imran Khan’s Tweets

Sentiment Analysis of Imran Khan’s Tweets

Analyzing Language Patterns for Depression Detection on Social Media: Insights from Reddit Data and Machine Learning Techniques

Employing Machine Learning for Multi-perspective Emotional Health Analysis

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