A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset

Dutta, Pijush; Paul, Shobhandeb; Cengiz, Korhan; Anand, Rishabh; Kumar, Asok

doi:10.1016/b978-0-323-90277-9.00002-x

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…In Reference 19, the authors utilize LSTM and multilayer perceptron with ANN (MLP‐ANN) for improving EEG data classification. Khan 20 establishes cross‐lingual emotion detection in Italian, English, Urdu, and German.…”

Section: Related Workmentioning

confidence: 99%

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Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Alohali,

Alasmari,

Almalki

et al. 2023

Trans Emerging Tel Tech

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Complex, intelligent systems, namely social network services (SNS), internet‐of‐things (IoT), augmented reality (AR), virtual reality (VR), and so on have become a crucial part of our day‐to‐day life. The dynamics of this system alter human behaviors in various forms. Therefore, it is important to understand the fundamental dynamics of the system and what it involves for user sentiment. Emotion analysis is a commonly used and very powerful analysis methodology in data mining. It gives an outstanding option to evaluate, determine, understand, and monitor the sentiments of consumers with respect to a service or a product. This article focuses on the design of textual emotion analysis using the improved metaheuristics with deep learning (TEA‐IMWDL) model for intellectual systems. The goal of the TEA‐IMWDL technique lies in the identification and classification of emotions that exist in social media content. To attain this, the presented TEA‐IMWDL technique undergoes preprocessing to make the input data compatible with the latter stages of processing. For the word embedding process, the Fast text approach is utilized in this study. Next, the long short term memory‐recurrent neural network (LSTM‐RNN) method can be enforced for the emotion classification procedure. To boost the classification efficiency of the LSTM‐RNN model, an improved gravitational search optimization algorithm (IGSA) based hyperparameter tuning process is involved in this study. A brief set of simulation analyses of the TEA‐IMWDL technique is carried out and the results are tested on a benchmark dataset. The experimental outcomes demonstrate the enhanced performance of the TEA‐IMWDL technique over other recent approaches.

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Section: Related Workmentioning

confidence: 99%

“…Step 4: output the better outcomes. EndIn Equation(19), f i (t) denotes the fitness value of particle x i (t), best(t), and worst (t) characterize both fitness values among every particle, correspondingly, and it is determined by the succeeding expression…”

mentioning

confidence: 99%

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Alohali,

Alasmari,

Almalki

et al. 2023

Trans Emerging Tel Tech

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show abstract

“…Typically, these methods map contrasting phrases onto adjacent vectors, despite the fact that the words' true meanings are diametrically opposed to one another. For SA in a variety of settings, a unique neural word embedding-based strategy was proposed [12]. They fixed one of the biggest problems with previous approaches, which was how poorly they fared when applied to domains other than the one in which they were initially taught.…”

Section: Literature Surveymentioning

confidence: 99%

Novel Method for Sentiment Analysis in Social Media Data Using Hybrid Deep Learning Model

Harika vanam,

Jeberson Retna Raj

2023

ARASET

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It is common practise to employ a contextual mining approach called sentiment analysis (SA) to glean subjective but potentially helpful information from textual data. In order to recognise, analyse, and extract answers, states, or emotions from the data, it employs Natural Language Processing (NLP), language understanding, forensics, and cognitive science. Significant progress towards a better SA model may be made using the features analysis method. In recent years, feature extractions have made extensive use of GloVe and Word2vec embedding models. They require a huge corpora of text data for training and creating accurate vectors, but they ignore emotional and contextual information in the text. Out-of-Vocabulary (OOV) words are not considered while creating these vectors, hence some information may be lost if these methods are used. The limited availability of annotated data is another obstacle to sentiment categorization. Misclassification may occur when there is a discrepancy between the review and the label. In this research, we offer a hybrid SA model capable of overcoming the challenges posed by missing ratings and reviews due to noise, out-of-context phrases, and context. To investigate and conduct sentiment and appropriate analysis, this study proposes an Autoencoder Bi-directional Recurrent Neural Network (ABRNN) based on Bi-directional Encoding from Transformers (BET). The reviews are initially categorised by their polarity ratings using the zero-shot categorization. Then, the data is fed into a pre-trained BET system to extract embeddings based on the semantics and context of sentences. The neural network, made up of expanded and hybrid LSTM, was then fed the acquired contextual embedded vectors. For extracting both global and local spatial contextual characteristics from the embedded data, this approach employs expanded approach in place of traditional approach. The complete phrase sequencing is performed with the help of Hybrid Long Short-Term Memory (HLSTM). Measures of accuracy, precision, recall, f1-score, and area under the curve (AUC) are used to assess the ABRNN model on four separate text datasets from different domains. Because of this, ABRNN may be utilised well for SA processes on social media evaluations, with no loss of data.

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Emotion Recognition: A New Tool for Healthcare Using Deep Learning Algorithms

Mridha,

Sarker,

Zaman

et al. 2024

Lecture Notes in Electrical Engineering

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A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave dataset

Cited by 7 publications

References 31 publications

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Novel Method for Sentiment Analysis in Social Media Data Using Hybrid Deep Learning Model

Emotion Recognition: A New Tool for Healthcare Using Deep Learning Algorithms

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