Emotion fusion for mental illness detection from social media: A survey

Zhang, Tianlin; Yang, Kailai; Ji, Shaoxiong; Ananiadou, Sophia

doi:10.1016/j.inffus.2022.11.031

Cited by 31 publications

(9 citation statements)

References 112 publications

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“…One such field to extract opinions, sentiments, and emotions is NLP, an entire field leveraging algorithms to understand human language, and the results have been quite fascinating. Leveraging sentiment extracted from social media, specifically Twitter, successfully predicted the 2016 US election results with an 81% model accuracy [10]. Similarly, Bollen achieved an accuracy of 87.6% in predicting the Dow Jones Index using various emotional dimensions extracted from tweets, reinforcing that prediction of the stock market is possible [11].…”

Section: The Methods and Results Of Previous Researchmentioning

confidence: 77%

Implementing NLP techniques and data for Europe financial market forecasting

Su,

Xiang,

Lai

et al. 2023

ACE

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The large military conflict between Russia and Ukraine has consequences for the global financial market. The European region is one of the hardest hits, which experienced a high energy cost and devaluation of the currency. This paper investigates the Employment Expectations Indicator (EEI) and Economic Sentiment Indicator (ESI) from the business consumer survey index to reflect the reaction of the consumers of the European market by implementing Natural Language Processing (NLP) techniques and machine learning. In the object, the monetary policy decisions from European Central Bank are considered the data set for forecasting the Index. In the result, all nine models from scikit-learn contributed a great job in both categories of Accuracy, Precision, and F-score and Recall with a level of 90% as average. It represents that the NLP techniques effectively forecast the future value of two European business and consumer indexes. However, the limit of the dataset and model mostly related to the performance of NLP techniques is working for forecasting but less related to the expected values of the EEI and ESI indices. Future research would focus more on forecasting the expected value of the two indexes and reflect the consumers' reactions in the markets.

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Section: The Methods and Results Of Previous Researchmentioning

confidence: 77%

Implementing NLP techniques and data for Europe financial market forecasting

Su,

Xiang,

Lai

et al. 2023

ACE

View full text Add to dashboard Cite

show abstract

“…Also, individualities may have different ways of expressing their symptoms, and there may be private rudiments involved in the individual process. [2]Still, with a thorough evaluation by a good internal health professional using established individual criteria and assessment tools, the delicacy of diagnosing internal illness can be fairly high.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorder using Machine learning Approach

Reddy,

Satvika

2023

Preprint

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Recently, the increase in internal health problems in society has led to an increase in research on the development of mechanistic capacity models to detect or predict internal mental health. The effective use of internal health assessments or discovery models allows internal health interpreters to redefine internal suffering more objectively than ever before, and in the early stages when interventions may be more effective.In this work, we aim to apply a bias mitigation system based on multitasking literacy to perform a fairness analysis and to fear the predicted model using the Reddit dataset. This article employs an efficient technique for machine learning random forests. The proposed model was evaluated against various performance metrics and the model showed 91.00% accuracy. This is an advantage compared to existing approaches.

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“…At present, individuals have the ability to express various types of information, including facts, opinions, emotions, and the intensity of emotions, through concise written messages on a wide range of topics [1]. The analysis of emotions expressed in social media content has garnered interest from researchers in the field of NLP technique [2]. Emotion analysis has a wide range of applications in various fields such as commerce, public health, social welfare, and policy [3].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

Sultan

2023

RIA

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Given the widespread incorporation of social media into everyday existence, platforms such as Twitter have become crucial arenas for individuals to articulate their thoughts, emotions, and viewpoints. The ability to identify emotions in these facial expressions has a wide range of practical uses, including tailored marketing strategies and research on human behaviour. Nevertheless, the language used on these platforms is frequently filled with colloquialisms and vagueness, rendering the process of detecting emotions a challenging endeavour even for individuals. The difficulties in analysing emotions on Twitter are particularly noticeable because current natural language processing (NLP) techniques have limited ability to handle slang language, and earlier classifiers that rely on slang have produced unsatisfactory results. This paper presents a new model for categorising emotions in tweets that contain slang. The model combines multiple approaches and utilises the WordNET dataset. The WordNET library is used in the proposed model to create synonymous phrases for the sentences in the text. The text data is divided into segments, reduced to their root forms, then filtered to remove often used words using natural language processing (NLP) approaches. The textual properties are described by utilising Term Frequency -Inverse Document Frequency (TF-IDF) and n-gram based similarity approaches. Emotions are classified by the utilisation of a convolutional neural network (CNN). An analysis was conducted on the performance of this model based on the metrics of classification accuracy and processing speed. The findings revealed a remarkable level of accuracy in classifying emotions in slang language using the suggested model, achieving a precise categorization rate of 89.3%. This study represents a notable advancement in the field of emotion classification in social media writing that contains slang language.

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Emotion fusion for mental illness detection from social media: A survey

Cited by 31 publications

References 112 publications

Implementing NLP techniques and data for Europe financial market forecasting

Implementing NLP techniques and data for Europe financial market forecasting

An Efficient Early Diagnosis and Healthcare Monitoring System for Mental Disorder using Machine learning Approach

An Enhanced Emotion Classification Scheme for Twits Based on Deep Learning Approach

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