A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach

Rajabi, Zeinab; Valavi, MohammadReza; Hourali, Maryam

doi:10.1007/s12559-020-09729-1

Cited by 8 publications

(4 citation statements)

References 54 publications

(70 reference statements)

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“…The similarity between image words is related to the commonality and difference between words: the higher the commonality, the higher the similarity; the higher the difference, the lower the similarity. Word-similarity computations are widely used in natural language processing [44], intelligent retrieval [45], data mining [46], and other fields. The image vocabulary similarity and lexical similarity calculations differ.…”

Section: Eye Trackingmentioning

confidence: 99%

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Zhang,

Liu,

Yang

et al. 2023

Applied Sciences

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Effective product styling designs must increasingly address users’ emotional requirements. This study introduces a product styling design method combining electroencephalography (EEG) and eye tracking for multimodal measurement based on the Kansei engineering theory. The feasibility of determining a target image using a similarity calculation model is verified. An experimental paradigm based on implicit measures is presented for product styling cognition research. This paradigm involves determining the target image, sample selection, target image matching experiments, and product styling cognition experiments. Based on the combined EEG and eye-tracking measurements, insights into product-form cognition are deduced to provide a scientific basis for product-form innovation design. Notably, variations in event-related potential during user cognition of product styling are more evident in the product-styling perception phase than in the evaluation phase. In the styling perception phase, samples with “high match” with the target image elicit more pronounced EEG responses than those with “low match”. These findings demonstrate the viability of understanding product-form cognition through multimodal implicit measurements, addressing issues such as the pronounced subjectivity inherent in traditional methods. Furthermore, this approach provides a pioneering technique for Kansei engineering research and offers a methodology for multimodal implicit measurements of product innovation design.

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Section: Eye Trackingmentioning

confidence: 99%

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Zhang,

Liu,

Yang

et al. 2023

Applied Sciences

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“…Using a polarity measure, the sentic vector was then converted to a polarity score in the range [–1, 1]. The sentic vectors for each thought were derived from the Hourglass of emotions, which classifies sentiments into four categories [ 42 ].…”

Section: Related Researchmentioning

confidence: 99%

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Alanazi

Khaliq

Ahmad

et al. 2022

IJERPH

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Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.

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“…The aforementioned studies on polarity detection and emotion recognition are limited to the analysis of textual data. When analyzing texts, however, the algorithms deal only with words, phrases and relationships, which are often not sufficient to interpret affective content [24], especially in more challenging tasks such as polarity disambiguation [25] and sarcasm detection [26,27]. One approach to overcoming these problems is to use fuzzy logic [28], which depends on approximate reasoning.…”

Section: Affective Computing and Sentiment Analysismentioning

confidence: 99%

When Old Meets New: Emotion Recognition from Speech Signals

et al. 2021

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Speech is one of the most natural communication channels for expressing human emotions. Therefore, speech emotion recognition (SER) has been an active area of research with an extensive range of applications that can be found in several domains, such as biomedical diagnostics in healthcare and human–machine interactions. Recent works in SER have been focused on end-to-end deep neural networks (DNNs). However, the scarcity of emotion-labeled speech datasets inhibits the full potential of training a deep network from scratch. In this paper, we propose new approaches for classifying emotions from speech by combining conventional mel-frequency cepstral coefficients (MFCCs) with image features extracted from spectrograms by a pretrained convolutional neural network (CNN). Unlike prior studies that employ end-to-end DNNs, our methods eliminate the resource-intensive network training process. By using the best prediction model obtained, we also build an SER application that predicts emotions in real time. Among the proposed methods, the hybrid feature set fed into a support vector machine (SVM) achieves an accuracy of 0.713 in a 6-class prediction problem evaluated on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset, which is higher than the previously published results. Interestingly, MFCCs taken as unique input into a long short-term memory (LSTM) network achieve a slightly higher accuracy of 0.735. Our results reveal that the proposed approaches lead to an improvement in prediction accuracy. The empirical findings also demonstrate the effectiveness of using a pretrained CNN as an automatic feature extractor for the task of emotion prediction. Moreover, the success of the MFCC-LSTM model is evidence that, despite being conventional features, MFCCs can still outperform more sophisticated deep-learning feature sets.

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A Context-Based Disambiguation Model for Sentiment Concepts Using a Bag-of-Concepts Approach

Cited by 8 publications

References 54 publications

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Product Styling Cognition Based on Kansei Engineering Theory and Implicit Measurement

Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

When Old Meets New: Emotion Recognition from Speech Signals

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