Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Saganowski, Stanisław

doi:10.3390/electronics11030496

Cited by 42 publications

(19 citation statements)

References 92 publications

(99 reference statements)

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“…The word is connected to other nodes through dependencies. In RNN, what is computed from all its children using a weight matrix is a vector representation of each node [27].…”

Section: Sentiment Category Outputmentioning

confidence: 99%

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

Jin

2022

Wireless Communications and Mobile Computing

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In English education, British and American literature is a new type of course. The teaching of British and American literature has also undergone many reforms. In the practice of teaching reform, artificial intelligence- (AI-) assisted teaching such as machine learning (ML) has a long history. The performance is continuously improved by studying the mechanism of computer simulation of the human brain learning British and American literature. Then, computer intelligence can be realized. Based on this, this paper mainly discusses two aspects. One is the sentiment tendency analysis method based on the sentiment dictionary, and the other is the sentiment tendency analysis method based on ML. It mainly introduces the judgment of different emotional tendencies by the sentiment analysis model, which is an automatic review analysis and ensemble classification approach. The improvement of sentiment analysis improves the recognition range of text sentiment words in British and American literature teaching to optimize the process of text analysis. Its main feature is that the sentiment analysis of text directly acts on the tendency of words, with fine granularity and accurate analysis. Finally, it is concluded that the maximum value of the algorithm proposed here is 0.9, which has higher accuracy than the maximum value of 0.81 of other analysis models. The results indicate that the integrated classification model combining British and American literature teaching with the dimensions hidden Markov model has relatively reasonable text analysis and high sentiment classification accuracy. In terms of British and American literature teaching, using ML algorithms can effectively help teachers teach British and American literature through sentiment analysis.

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“…The word is connected to other nodes through dependencies. In RNN, what is computed from all its children using a weight matrix is a vector representation of each node [27].…”

Section: Sentiment Category Outputmentioning

confidence: 99%

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

Jin

2022

Wireless Communications and Mobile Computing

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“…Research with neurophysiological sensors [ 12 , 13 ] has begun to aid in monitoring schizophrenia [ 14 , 15 ], Parkinson’s’ disease [ 16 ], traumatic brain injury [ 17 ], physiological signals [ 18 ], cognitive function [ 19 ], epileptic seizures [ 20 ], alcoholism [ 21 ], brain tumors [ 22 ], brain cancer [ 23 ], mental stability [ 24 ], personality [ 25 ], eye tracking [ 26 ], and many other phenomena. Moreover, studies conducted on EEG data processing have identified human emotions with exceptional accuracy [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. These studies are the backbone of the growing number of emotional recognition applications, and because of their far-reaching applications, the societal impact of enhancing their efficiency, accuracy, and precision cannot be overstated.…”

Section: Literature Reviewmentioning

confidence: 99%

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Akter

Prodhan

Pias

et al. 2022

Sensors

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Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.

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“…The rapid development and increasingly widespread use of artificial intelligence technologies has led to a significant increase in interest and development of systems that automatically recognize affective states. These systems are designed to recognize, accurately interpret, and respond to the recognized emotional states of the communicating individual [ 2 , 3 , 4 , 5 , 6 ]. There is an entire class of solutions called multimodal-based affective human–computer interaction that enables computer systems to recognize specific affective states.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications

Płaza

Trusz

Kęczkowska

et al. 2022

Sensors

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Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%).

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Bringing Emotion Recognition Out of the Lab into Real Life: Recent Advances in Sensors and Machine Learning

Cited by 42 publications

References 92 publications

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

Sentiment Analysis in British and American Literature Teaching under Formative Assessment and Machine Learning

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications

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