A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis

Kottursamy, Kottilingam

doi:10.36548/jtcsst.2021.2.003

Cited by 72 publications

(10 citation statements)

References 25 publications

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“…Research results showed that the designed algorithm is superior to traditional machine learning algorithms in sentiment classification and comment rating prediction ( Luo and Xu, 2021 ). Kottursamy (2021) studied the role of facial expression recognition in social science and HCI, discussed various common deep learning algorithms for emotion recognition, and established a new CNN model based on the eXnet library to achieve higher facial recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

Zhong

Zhang

et al. 2022

Front. Psychol.

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In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

Zhong

Zhang

et al. 2022

Front. Psychol.

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“…e classifier outputs the final result of the integration in the form of an array. Since the research object is multiemotion classification, the content in the array represents the probability of multiple emotions in the text [27,28]. e softmax classifier calculates the probability that a sample belongs to a certain category as follows:…”

Section: Classification Outputmentioning

confidence: 99%

Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

Yan

Zhou

Qian

et al. 2021

Scientific Programming

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For most current sentiment analysis models, it is difficult to capture the complex semantic and grammatical information in the text, and they are not fully applicable to the analysis of student sentiments. A novel student text sentiment analysis model using the convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism, called CNN-BiGRU-AT model, is proposed. Firstly, the text is divided into multiple sentences, and the convolutional neural network (CNN) is used to extract n-gram information of different granularities from each sentence to construct a sentence-level feature representation. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. Finally, an attention mechanism is added to the CNN-BiGRU model, and different learning weights are applied to the model by calculating the attention score. The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated. The results show that the accuracy rate and recall rate of its classification mostly exceed 0.9, and the F1 value is not lower than 0.8, which are better than the results of other models. The proposed model can provide a certain reference for the related students’ text sentiment analysis research.

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“…Thus, the domain of FER has been studied broadly for the past 10 years [5]. Currently, with the rise of appropriate data and continuous progression of deep learning (DL), a FER mechanism that precisely identifies facial expressions in several surroundings is being studied actively [6]. FER depends on an evolutionary and ergonomic technique.…”

Section: Introductionmentioning

confidence: 99%

Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

Sharmili¹,

Yonbawi²,

Alahmari³

et al. 2023

Computer Systems Science and Engineering

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Facial expression recognition (FER) remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass variations. Conventional techniques for this problem depend on hand-crafted features, namely, LBP, SIFT, and HOG, along with that a classifier trained on a database of videos or images. Many execute perform well on image datasets captured in a controlled condition; however not perform well in the more challenging dataset, which has partial faces and image variation. Recently, many studies presented an endwise structure for facial expression recognition by utilizing DL methods. Therefore, this study develops an earthworm optimization with an improved SqueezeNet-based FER (EWOISN-FER) model. The presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization (CLAHE) technique as a pre-processing step. In addition, the improved SqueezeNet model is exploited to derive an optimal set of feature vectors, and the hyperparameter tuning process is performed by the stochastic gradient boosting (SGB) model. Finally, EWO with sparse autoencoder (SAE) is employed for the FER process, and the EWO algorithm appropriately chooses the SAE parameters. A wide-ranging experimental analysis is carried out to examine the performance of the proposed model. The experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

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A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis

Cited by 72 publications

References 25 publications

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education

Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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