A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email

Halim, Zahid; Waqar, Mehwish; Tahir, Madiha

doi:10.1016/j.knosys.2020.106443

Cited by 46 publications

(20 citation statements)

References 35 publications

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“…Texts in modern electronic resources have certain specifics, e.g., they can be limited by the length of the text. The work [9] applies machine learning methods and uses three classifiers and three feature selection methods. A special feature of the proposed approach is the use of intra-text features to identify emotions contained in short texts, and the development of a dataset for this purpose.…”

Section: Semantic Analysis After Extraction Of Publication Informationmentioning

confidence: 99%

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Yusupova

Bogdanova

Komendantova

et al. 2021

Digital

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The topic of affective computing has been growing rapidly in recent times. In the last five years, the volume of publications in this field has tripled. The question arises which research trends are most in demand today. This can only be judged by analysing the publications that present the results of research. Since researchers have access to the entire global scientific publication space, the task of analysing big data arises. This leads to the problem of identifying the most significant results in the subject area of interest. This paper presents some results of the analysis of semi-structured information from scientific citation databases on the subject of “affective computing”.

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Section: Semantic Analysis After Extraction Of Publication Informationmentioning

confidence: 99%

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Yusupova

Bogdanova

Komendantova

et al. 2021

Digital

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“…In this era, emotion analysis is the most popular in various fields, such as natural language processing, 14 bioinformatics, 15 text mining, 16 and computer vision 17 that allow the evaluation of supervised and unsupervised data 18 . The excursus of emotion recognition systems includes sentiment analysis, which is utilized to identify the emotional role of the multimedia data, which may be positive, negatives, and neutral 19 .…”

Section: Introductionmentioning

confidence: 99%

Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

Mustaqeem

Kwon

2021

Int J of Intelligent Sys

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Speech signal processing is an active area of research, the most dominant source of exchanging information among human beings, and the best way for human–computer interaction (HCI). Human behavior assessments and emotion recognition from a speech signal, such as speech emotion recognition (SER) is an emerging HCI area of exploration with various real time claims. The performance of an efficient SER system depends on feature learning, which include salient and discriminative information such as high‐level deep features. In this paper, we proposed a two‐stream deep convolutional neural network with an iterative neighborhood component analysis (INCA) to learn mutually spatial‐spectral features and select the most discriminative optimal features for the final prediction. Our model is composed of two channels, and each channel is associated with the convolutional neural network structure to extract cues from the oral signals. The first channel extracts feature from the spectral domain, and the second channel extracts features from the spatial domain, which are then fused and fed to the INCA to remove the severance and select the optimal features for the final model training. The joint refine features are passed from the fully connected network with a softmax classifier to yield the predictions of the different emotions. We trained our proposed system using three benchmarks, which included the EMO‐DB, SAVEE, and RAVDESS emotional speech corpora, and we tested the prediction performance to secure 95%, 82%, and 85% recognition rates. The performance of the system shows the effectiveness and significance of the proposed system.

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“…The voltage fluctuations captured by EEG sensors are interpreted by the BCI system based on some algorithm to produce the desired outputs. These outputs could be in the form of computer commands reflecting the subject's active intent, or they may indicate the subject's mental conditions like feelings, emotions [16], anxiety, stress, or depression. This may also indicate a type of mental disorder that the subject is suffering from, like schizophrenia or attention deficit hyperactivity disorder (ADHD), to name a few.…”

Section: Introductionmentioning

confidence: 99%

“…This gives more flexibility and insight into the nature of the signals. The most commonly used frequency bands in EEG analysis are delta (0.5-4 Hz), theta (4-8 Hz), alpha (8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Band power refers to the power contained in a range of frequencies.…”

Section: Introductionmentioning

confidence: 99%

Imagined character recognition through EEG signals using deep convolutional neural network

Ullah

Halim

2021

Med Biol Eng Comput

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Electroencephalography (EEG)-based brain computer interface (BCI) enables people to interact directly with computing devices through their brain signals. A BCI typically interprets EEG signals to reflect the user's intent or other mental activity. Motor imagery (MI) is a commonly used technique in BCIs where a user is asked to imagine moving certain part of the body such as a hand or a foot. By correctly interpreting the signal, one can perform a multitude of tasks such as controlling wheel chair, playing computer games, or even typing text. However, the use of motor-imagery-based BCIs outside the laboratory environment is limited due to the lack of their reliability. This work focuses on another kind of mental imagery, namely, the visual imagery (VI). VI is the manipulation of visual information that comes from memory. This work presents a deep convolutional neural network (DCNN)-based system for the recognition of visual/mental imagination of English alphabets so as to enable typing directly via brain signals. The DCNN learns to extract the spatial features hidden in the EEG signal. As opposed to many deep neural networks that use raw EEG signals for classification, this work transforms the raw signals into band powers using Morlet wavelet transformation. The proposed approach is evaluated on two publicly available benchmark MI-EEG datasets and a visual imagery dataset specifically collected for this work. The obtained results demonstrate that the proposed model performs better than the existing state-of-the-art methods for MI-EEG classification and yields an average accuracy of 99.45% on the two public MI-EEG datasets. The model also achieves an average recognition rate of 95.2% for the 26 English-language alphabets.

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A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email

Cited by 46 publications

References 35 publications

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Extracting Information on Affective Computing Research from Data Analysis of Known Digital Platforms: Research into Emotional Artificial Intelligence

Optimal feature selection based speech emotion recognition using two‐stream deep convolutional neural network

Imagined character recognition through EEG signals using deep convolutional neural network

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