Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Schmidt, Thomas; Schlindwein, M.; Lichtner, Katharina; Wolff, Christian

doi:10.1515/icom-2020-0009

Cited by 12 publications

(9 citation statements)

References 36 publications

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“…Experiments show that the recognition rate of multimodal features is higher than that of one modal feature. Schmidt et al [ 24 ] put forward the multistream fusion hidden Markov model for emotion recognition. The multistream fusion hidden Markov model is the generalization of the two-stream fusion hidden Markov model, which is a general model-level modal fusion method.…”

Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Pang

Bao

2022

Computational Intelligence and Neuroscience

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“…Recent research has increasingly focused on unsupervised and semisupervised learning algorithms. Core algorithms such as MLP, Support Vector Machine (SVM), and Logistic Regression are all trained on high-dimensional sparse feature vectors [18]. Reference [19] proposed a method of emotion recognition based on deep learning combined with semisupervised learning of long-and shortterm memory.…”

Section: Related Researchmentioning

confidence: 99%

“…[17] Ref. [18] Proposed model Ref. [13] Figure 4: Comparison of experimental results of predicting students' emotional distribution.…”

Section: Reader Sentiment Distribution Predictionmentioning

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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“…There is a map-reduce-based e-commerce user opinion recognition framework, and new dictionary-based technology is used to mine the products. Neutral evaluation to correct the results of these opinions being classified as positive or negative, to reduce the error of sentiment analysis (Bhagat and Mane, 2019 ; Schmidt et al, 2020 ).…”

Section: Dynamic Evolution Mechanism Of Digital Entrepreneurship Ecosystem Based On Text Sentiment Calculation Analysismentioning

confidence: 99%

Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis

Yao

2021

Front. Psychol.

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To solve the limitations of the current entrepreneurial ecosystem, the research on the digital entrepreneurial ecosystem is more meaningful. This article aims to study the dynamic evolution mechanism of the digital entrepreneurship ecosystem based on text sentiment computing analysis. It proposes an improved Bi-directional long short-term memory (Bi-LSTM) model, which uses a multilayer neural network to deal with classification problems. It has a higher accuracy rate, recall rate, and F1 value than the traditional LSTM model and can better perform sentiment analysis on text. The algorithm uses the optimized Naive Bayes algorithm, which is based on Euclidean distance weighting and can assign different weights to the final classification results according to different attributes. Compared with the general Bayes algorithm, it improves the calculation efficiency and can better match the digital entrepreneurial ecosystem, which is evolving dynamically, predicting and analyzing its future development. The experimental results in this article show that the improved Bi-LSTM is better than the traditional Bi-LSTM model in terms of accuracy and F1 value. The accuracy rate is increased by 1.1%, the F1 value is increased by 0.6%, and the recall rate is only <0.2%. Running on the Spark platform, although 3% accuracy is sacrificed, the running time is increased by 320%. Compared with the traditional cellular neural network (CNN) algorithm, the accuracy rate is increased by 4%, the recall rate is increased by 14%, and the F1 value is increased by 9%, which proves that it has a strong non-linear fitting ability. The performance improvement brought by the huge data set is very huge, which fully proves the feasibility of the digital entrepreneurship ecosystem.

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Investigating the Relationship Between Emotion Recognition Software and Usability Metrics

Cited by 12 publications

References 36 publications

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers

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

Dynamic Evolution Mechanism of Digital Entrepreneurship Ecosystem Based on Text Sentiment Computing Analysis

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