Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile

This study explores the influence of social media content on societal attitudes and actions during critical events, with a special focus on occurrences in Chile, such as the COVID-19 pandemic, the 2019 protests, and the wildfires in 2017 and 2023. By leveraging a novel tweet dataset, this study introduces new metrics for assessing sentiment, inclusivity, engagement, and impact, thereby providing a comprehensive framework for analyzing social media dynamics. The methodology employed enhances sentiment classification through the use of a Deep Random Vector Functional Link (D-RVFL) neural network, which demonstrates superior performance over traditional models such as Support Vector Machines (SVM), naive Bayes, and back propagation (BP) neural networks, achieving an overall average accuracy of 78.30% (0.17). This advancement is attributed to deep learning techniques with direct input–output connections that facilitate faster and more precise sentiment classification. This analysis differentiates the roles of influencers, press radio, and television handlers during crises, revealing how various social media actors affect information dissemination and audience engagement. By dissecting online behaviors and classifying sentiments using the RVFL network, this study sheds light on the effects of the digital landscape on societal attitudes and actions during emergencies. These findings underscore the importance of understanding the nuances of social media engagement to develop more effective crisis communication strategies

Developing a hybrid hidden MARKOV model using fusion of ARMA model and artificial neural network for crude oil price forecasting

Crude oil price forecasting is an important component of sustainable development of many countries as crude oil is an unavoidable product that exist on earth. Crude oil price forecasting plays a very vital role in economic development of many countries in the world today. Any fluctuation in crude oil price tremendously affects many economies in terms of budget and expenditure. In view of this, it is of great concern by economists and financial analysts to forecast such a vital commodity. However, Hidden Markov Model, ARMA Model and Artificial Neural Network has many drawbacks in forecasting such as linear limitations of ARMA model which is in contrast to the financial time series which are often nonlinear, ANN is very weak in terms of out-sample forecast and it has very tedious process of implementation, HMM is very weak in an in-sample forecast and has issue of a large number of unstructured parameters. In view of this drawbacks of these three models (ANN, ARMA and HMM), we developed an efficient Hybrid Hidden Markov Model using fusion of ARMA Model and Artificial Neural Network for crude oil price forecasting, MATLAB was employed to develop the four models (Hybrid HMM, HMM, ARMA and ANN). The models were evaluated using three different evaluation techniques which are Mean Absolute Percentage Error (MAPE), Absolute Error (AE) and Root Mean Square Error (RMSE). The findings showed that Hybrid Hidden Markov Model was found to provide more accurate crude oil price forecast than the other three models in which. The results of this study indicate that Hybrid Hidden Markov Model using fusion of ARMA and ANN is a potentially promising model for crude oil price forecasting

Empirical validation of ELM trained neural networks for financial modelling

The purpose of this work is to compare predictive performance of neural networks trained using the relatively novel technique of training single hidden layer feedforward neural networks (SFNN), called Extreme Learning Machine (ELM), with commonly used backpropagation-trained recurrent neural networks (RNN) as applied to the task of financial market prediction. Evaluated on a set of large capitalisation stocks on the Australian market, specifically the components of the ASX20, ELM-trained SFNNs showed superior performance over RNNs for individual stock price prediction. While this conclusion of efficacy holds generally, long short-term memory (LSTM) RNNs were found to outperform for a small subset of stocks. Subsequent analysis identified several areas of performance deviations which we highlight as potentially fruitful areas for further research and performance improvement

Efficient uniform approximation using Random Vector Functional Link networks

A Random Vector Functional Link (RVFL) network is a depth-2 neural network with random inner weights and biases. As only the outer weights of such architectures need to be learned, the learning process boils down to a linear optimization task, allowing one to sidestep the pitfalls of nonconvex optimization problems. In this paper, we prove that an RVFL with ReLU activation functions can approximate Lipschitz continuous functions provided its hidden layer is exponentially wide in the input dimension. Although it has been established before that such approximation can be achieved in $L_2$ sense, we prove it for $L_\infty$ approximation error and Gaussian inner weights. To the best of our knowledge, our result is the first of this kind. We give a nonasymptotic lower bound for the number of hidden layer nodes, depending on, among other things, the Lipschitz constant of the target function, the desired accuracy, and the input dimension. Our method of proof is rooted in probability theory and harmonic analysis

Modern Problems in Mathematical Signal Processing: Quantized Compressed Sensing and Randomized Neural Networks

We study two problems from mathematical signal processing. First, we consider problem of approximately recovering signals on a smooth, compact manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using $\Sigma\Delta$ or distributed noise-shaping schemes. We construct a convex optimization algorithm for signal recovery that, given a Geometric Multi-Resolution Analysis approximation of the manifold, guarantees signal recovery with high probability. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians.Second, we consider the problem of approximation continuous functions on compact domains using neural networks. The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of B.~Igelnik and Y.H.~Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. We begin to fill this theoretical gap by providing a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with $\varepsilon$-error convergence rate inversely proportional to the number of network nodes; we then extend this result to the non-asymptotic setting using a concentration inequality for Monte-Carlo integral approximations. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic cases