Fine-Grained Emotion Analysis Based on Mixed Model for Product Review

Nowadays, with the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. A large number of reviews made it difficult for manufacturers or potential customers to track the comments and suggestions that customers made. This paper presents a method for extracting emotional elements containing emotional objects and emotional words and their tendencies from product reviews based on mixed model. First we constructed conditional random fields to extract emotional elements, lead-in semantic and word meaning as features to improve the robustness of feature template and used rules for hierarchical filtering errors. Then we constructed support vector machine to classify the emotional tendency of the fine-grained elements to achieve key information from product reviews. Deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure

Video elicited physiological signal dataset considering indoor temperature factors

IntroductionHuman emotions vary with temperature factors. However, most studies on emotion recognition based on physiological signals overlook the influence of temperature factors. This article proposes a video induced physiological signal dataset (VEPT) that considers indoor temperature factors to explore the impact of different indoor temperature factors on emotions.MethodsThis database contains skin current response (GSR) data obtained from 25 subjects at three different indoor temperatures. We selected 25 video clips and 3 temperatures (hot, comfortable, and cold) as motivational materials. Using SVM, LSTM, and ACRNN classification methods, sentiment classification is performed on data under three indoor temperatures to analyze the impact of different temperatures on sentiment.ResultsThe recognition rate of emotion classification under three different indoor temperatures showed that anger and fear had the best recognition effect among the five emotions under hot temperatures, while joy had the worst recognition effect. At a comfortable temperature, joy and calmness have the best recognition effect among the five emotions, while fear and sadness have the worst recognition effect. In cold temperatures, sadness and fear have the best recognition effect among the five emotions, while anger and joy have the worst recognition effect.DiscussionThis article uses classification to recognize emotions from physiological signals under the three temperatures mentioned above. By comparing the recognition rates of different emotions at three different temperatures, it was found that positive emotions are enhanced at comfortable temperatures, while negative emotions are enhanced at hot and cold temperatures. The experimental results indicate that there is a certain correlation between indoor temperature and physiological emotions

Wavelet packet analysis for speaker-independent emotion recognition

© 2020 Elsevier B.V. Extracting effective features from speech signals is essential to recognize different emotions. Recent studies have demonstrated that wavelet analysis is a useful technique in signal processing. In this study, we extract emotion features using wavelet packet analysis from speech signals for speaker-independent emotion recognition. We explore and evaluate these features from two databases, i.e., EMODB and EESDB. It is found that the extracted features are effective for recognizing various speech emotions. Furthermore, compared with common features such as Mel-Frequency Cepstral Coefficients (MFCC), these features can improve the recognition rates by 14.9 and 4.3 percentages on EMODB and EESDB, respectively

Video smoke detection using shape, color and dynamic features

Video smoke detection benefits life safety and environment protection. A method of video smoke detection using shape, color and dynamic texture features is presented in this paper. Firstly, an algorithm identifying cone geometry feature is used to distinguish conical region from dynamic regions. Secondly, conical regions are filtered by a color filtering algorithm to further test the candidate smoke region. Finally, a texture filtering algorithm is used to differentiate true smoke from candidate smoke regions. Experiments show that the proposed method is effective and it results in earlier and more reliable detection than the other two methods reported in the literature. The processing rate of the smoke detection method achieves 25 frames per second with an image size of 320x240 pixels

DataSheet_1_Quality control of Ganoderma lucidum by using C, H, O, and N stable isotopes and C and N contents for geographical traceability.docx

RationaleGanoderma lucidum (G. lucidum) is a popular medicinal fungus that has been used in traditional medicine for decades, with its provenance influencing its medicinal and commercial worth. The amount of active ingredients and the price of G. lucidum from different origins vary significantly; hence, fraudulent labeling is common. Reliable techniques for G. lucidum geographic verification are urgently required to safeguard the interests of consumers, producers, and honest dealers. A stable isotope is widely acknowledged as a useful traceability technique and could be developed to confirm the geographical origin of G. lucidum.MethodsG. lucidum samples from various sources and in varying stages were identified by using δ13C, δD, δ18O, δ15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum.ResultsIn the stage model, the δ13C, δD, δ18O, δ15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ13C, δD, δ18O, and δ15N). In the origin model, only four variables, namely δ13C, δD, δ18O, and δ15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%.ConclusionC, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.</p