The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

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Sentiment Classification Algorithm Based on Multi-Modal Social Media Text Information

Deep learning is a wildly popular topic in machine learning and is structured as a series of nonlinear layers that learns various levels of data representations. Deep learning employs numerous layers to represent data abstractions to implement various computer models. Deep learning approaches like generative, discriminative models and model transfer have transformed information processing. This article proposes a comprehensive review of various deep learning algorithms Multi layer perception, Self-organizing map and deep belief networks algorithms. It first briefly introduces historical and recent state-of-the-art reviews with suitable architectures and implementation steps. Moreover, the various applications of those algorithms in various fields such as wireless networks, Adhoc networks, Mobile ad-hoc and vehicular ad-hoc networks, speech recognition engineering, medical applications, natural language processing, material science and remote sensing applications, etc. are classified. 

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A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN

It is aimed to identify the basic success factors, which are essential for startups as they intend to develop successful and profitable business models over time. To this end, it is attempted to analyze the sentiments on user-generated content (UGC) on Twitter. First, trigram word cloud is used. Then, a sentiment analysis is done with various predictive models including random forest, support-vector machine (SVM) and multilayer perceptron (MLP) to test the labeling of unlabeled data. To divide topics into negative, positive, and neutral sentiments, latent Dirichlet allocation (LDA) has been applied. According to the results, the MLP method on the basis of accuracy criterion offers an accuracy of 0.81, which is higher than other tested methods. In this regard, random forest and SVM methods provide accuracy of 0.78 and 0.80, respectively. Voting and stacking algorithms were used to increase the accuracy of the algorithms. However, it is found that with the use of voting method, the accuracy is almost equal to the results obtained from the MLP and with stacking method the accuracy is less than all three methods. Using word cloud, it is indicated that the most negative trigram is startups innovation regarding climate change, the most positive one is product marketing management and business-related concepts are determined as neutral. It is found that startup acceleration process, pushing for quicker completion, delivering the best product at the beginning of the project, poor management practices, and focusing just on properties are grouped as negative sentiments. On the other hand, sustainable and innovative business plan, the presence of experienced entrepreneurs and investors, coronavirus (COVID-19), and innovation are recognized as positive sentiments, and no analysis is given for neutral sentiments.

Identifying key success factors for startups With sentiment analysis using text data mining

Metaphor plays an important role in human communication, which often conveys and evokes sentiments. Numerous approaches to sentiment analysis of metaphors have thus gained attention in natural language processing (NLP). The primary focus of these approaches is on linguistic features and text rather than other modal information and data. However, visual features such as facial expressions also play an important role in expressing sentiments. In this paper, we present a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach. For this, we create a Chinese dataset, containing textual data from metaphorical sentences along with visual data on synchronized facial images. The experimental results indicate that our multimodal model outperforms several other linguistic and visual models, and also outperforms the state-of-the-art methods. The contribution is realized in terms of novelty of the approach and creation of a new, sizeable, and scarce dataset with linguistic and synchronized facial expressive image data. The dataset is particularly useful in languages other than English and the approach addresses one of the most challenging NLP issue: sentiment analysis in metaphor.

In Your Face: Sentiment Analysis of Metaphor with Facial Expressive Features

A new trend, cloud computing, has moved computer model type away from hardware and for usefulness. However only a few of the key concerns are data security, privacy, faith and a secure cloud infrastructures that could jeopardise cloud computing's widespread acceptance and benefits. Virtualization is used in cloud computing to provide customers with scalable, dependable, as well as low-cost IT providers the hypervisor is at the heart of virtualization, It seems to be many to translate abstract hardware into actual hardware and make a device. The user will get access to the VM on a longer time while you pay less by reconfiguring (i.e. store the user's processor and memory use of VM in order to credit it at the end), contributing to the theft of operation. Because during its server stay there will be no trace of the user behaviour, this assault, which is triggered by stealing a VM, would have a financial effect on the cloud system, pose a greater danger to cloud storage performance. 

Mitigating theft-of-service attack - Ensuring cloud security on virtual machines

Numerous public networks, namely Instagram, YouTube, Facebook, Twitter, etc., share their own feelings and idea as videotapes, posts, and pictures. In future research, adapting to such data and mining valuable information from it will be an undeniably troublesome errand. This paper proposes a novel audio–video–textual-based multimodal sentiment analysis approach. The proposed approach investigates the sentiments that are collected from the web recordings that utilize audio, video, and textual modalities for further extraction. A feature-level fusion technique is employed in fusing the extracted features from different modalities. Therefore, the extracted features are optimally chosen by using a novel oppositional grass bee optimization (OGBEE) algorithm to obtain the best optimal feature set. Here, 12 benchmark functions are developed to validate the numerical efficiency and the effectiveness of a novel OGBEE algorithm for various aspects. Moreover, our proposed approach utilizes multilayer perceptron-based neural network (MLP-NN) for sentiment classification. The experimental analysis reveals that the proposed approach provides better classification accuracy of about 95.2% with less computational time.

M. Krishnamurthy

Papers

Novel OGBEE-based feature selection and feature-level fusion with MLP neural network for social media multimodal sentiment analysis