Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.

In this paper, we present a new wavelet shrinkage operator to remove noise for non-linear time series. Like other improvements, the proposed operator is used to address the discontinuity of the hard operator as well as the large bias of the soft operator. The special of this operator is that it introduces the universal threshold to divide the details of signal and noise. Such that the operator can identify those coefficients with noise, and then shrink larger for them, otherwise, shrink small. We test the denoising performance of the new operator in the chaotic time series generated by the Lorenz system. The results show that the new method successfully reduces the noise in nonlinear time series. Also, we applied our approach to improving the classification accuracy of the bearing fault in the noisy environment. The bearing fault datasets come from the Case Western Reserve University (CWRU) bearing data center and Paderborn University respectively. Experimental results show that the proposed operator effectively reduces the error rate on both traditional classifier models and deep learning models. Comparisons with the three existing shrinkage operators demonstrate the superiority of the proposal.

A flow channel structure design plays a significant role in an open-cathode proton exchange membrane fuel cell. The cell performance is sensitive to the structural parameters of the flow field, which mainly affects the heat and mass transfer between membrane electrode assembly and channel. This paper presents theoretical and experimental studies to investigate the impacts of anode flow field parameters (numbers of the serpentine channels, depths, and widths of the anode channel) on cell performance and temperature characteristics. The result indicates that the number of anode serpentine channels adjusts the pressure and flow rate of hydrogen in the anode flow channel effectively. The depth and width of the channel change the pressure, flow rate, and mass transfer capacity of hydrogen, especially under the high current density. There appears the best depth to achieve optimum cell performance. The velocity and concentration of hydrogen have important influences on the mass transfer which agrees with the anode channel structure design and performance changes based on the field synergy principle. This research has great significance for further understanding the relationship between anode flow field design and fuel cell performance in the open-cathode proton exchange membrane fuel cell stack.

The complexity of sleep disorder diagnosis continuously increases the clinical requirement for simultaneous measurement of sleep arousal and sleep stage, which, however, has not received enough attention from engineering scholars. To the best of our knowledge, all previous machine learning-based detection methods use the single view mechanism to identify either sleep arousal or sleep stage. In this article, a multiview hybrid neural network with semi-supervised learning (SSL), named as MSleepNet, is proposed for simultaneous sleep arousal and sleep stage detection. In particular, the features of single-channel electroencephalography (EEG) signal from both the time domain and frequency domain are extracted by the improved residual backbone network. Then, an attention mechanism is introduced to enhance the feature recognition ability in the frequency domain. A multitask classification loss function is also designed to synchronously consider the correlation between sleep arousal and sleep stage. In the classification part of the network, the supervised loss and semi-supervised loss for each task are effectively combined to alleviate the data imbalance problem and improve the classification accuracy. Overnight polysomnographic recordings from two public datasets (sleep heart health study (SHHS), $n=200$ and Physio2018, $n=100$ ) and one dataset from a local clinic ( $n=60$ ) were applied to validate MSleepNet. Experimental results on Physio2018 dataset demonstrate that MSleepNet achieves an overall accuracy of 0.78 and $F1$ score of 0.73 for sleep staging, and area under the precision-recall curve (AUPRC) of 0.39 and an area under the receiver operating characteristic curve (AUROC) of 0.75 for sleep arousal detection. Therefore, MSleepNet framework based on multiview hybrid neural network has shown its potential in intelligent sleep monitory for synchronizing sleep arousal and sleep stage measurement with only very limited labeled data.