Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care Perspective

De, Asmita; Mishra, Sushruta

doi:10.1007/978-981-19-1076-0_12

Cited by 11 publications

(2 citation statements)

References 45 publications

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“…It is responsible for giving insight into the emotion behind the words (Abirami & Gayathri, 2017). Sentiment analysis intends to regulate the settled way of thinking or feeling concerning contextual polarity (De & Mishra, 2022).…”

Section: Fundamental Steps Used In Sentiment Analysismentioning

confidence: 99%

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Naithani

Raiwani

2022

Expert Systems

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The leading intention of the current paper is to review the research work accomplished by various researchers to achieve sentiment analysis on the text and to elaborate on natural language processing (NLP) and various machine learning algorithms used to evaluate textual sentiments. In this study, primitive cases are considered that used crucial algorithms, and knowledge that can be opted for sentiment analysis. A survey of the work that has been done till now is conducted observing the results and outcomes concerning varying parameters of various researchers who worked on previously existing as well as novel and hybrid algorithms opting legion methodologies. The fundamental algorithms like Support Vector Machine (SVM), Bayesian Networks (BN), Maximum Entropy (MaxEnt), Conditional Random Fields (CRF) and Artificial Neural Networks (ANN) are also discussed to achieve practice percentage and accuracy score in the field of NLP, sentiment analysis and text analytics. Various other novel approaches and algorithms like CNN, LSTM, KNN, K*, K-means, K-means++, SOM and ENORA, along with their limitations and the performance metrics providing accuracies for major open data sets are also analyzed.

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Section: Fundamental Steps Used In Sentiment Analysismentioning

confidence: 99%

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Naithani

Raiwani

2022

Expert Systems

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“…Automated emotion detection is a significant research study that solves two subjects: artificial intelligence and human emotion recognition. The emotional state of a person can be gained from verbal and nonverbal data captured using the different sensors, for instance from facial changes, physiological signals, and tone of voice (De and Mishra, 2022).…”

Section: Introductionmentioning

confidence: 99%

Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People

Alshahrani,

Yaseen,

Drar

2024

Journal of Disability Research

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Emotion analysis is an effective method for improving interaction and understanding for speech-impaired people. We can provide the best interaction and support emotional well-being by analyzing the emotion conveyed through speech. Using deep learning (DL) or machine learning algorithms for training an emotion classification method. This might include training classifiers namely random forests, deep neural networks, or support vector machines. It is noteworthy that emotion analysis could be effective; however, it is crucial to consider individual differences and context while interpreting emotion. Furthermore, ensuring data protection and privacy and obtaining consent are vital features to consider while working with sensitive speech data. Therefore, this study presents an emotion analysis approach using improved cat swarm optimization with machine learning (EA-ICSOML) technique. The EA-ICSOML technique applies the concepts of computer vision and DL to identify various types of emotions. For feature vector generation, the ShuffleNet model is used in this work. To adjust the hyperparameters compared to the ShuffleNet system, the ICSO algorithm is used. Finally, the recognition and classification of emotions are performed using the Transient Chaotic Neural Network approach. The performance validation of the EA-ICSOML technique is validated on facial emotion databases. The simulation result inferred the improved emotion recognition results of the EA-ICSOML approach compared to other recent models in terms of different evaluation measures.

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Disease Detection and Risk Prediction System Based Web Application Using Machine Learning

Kumar,

Singh,

Sahoo

et al. 2023

Lecture Notes in Computer Science

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Augmented Intelligence in Mental Health Care: Sentiment Analysis and Emotion Detection with Health Care Perspective

Cited by 11 publications

References 45 publications

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Realization of natural language processing and machine learning approaches for text‐based sentiment analysis

Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People

Disease Detection and Risk Prediction System Based Web Application Using Machine Learning

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