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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Updated: Sep 6, 2025

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Deep Neural Networks Applied to Stock Market Sentiment Analysis.

Filipe Correia1,2, Ana Maria Madureira1,2, Jorge Bernardino3,4

  • 1Institute of Engineering of Porto (ISEP/P.PORTO), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida n\({^\underline{\circ}}\) 431, 4200-072 Porto, Portugal.

Sensors (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

Deep Learning effectively analyzes Big Data for Stock Market Sentiment Analysis. Convolutional and long short-term memory layers show promise, achieving high accuracy and a positive return on investment in simulations.

Keywords:
Big DataDeep LearningSentiment Analysisfinancial marketssocial networksstock data

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Computational Finance

Background:

  • Exponential growth in data volume across diverse sources presents significant extraction challenges.
  • Understanding the impact of Big Data on decision-making, particularly in financial markets, is crucial.

Purpose of the Study:

  • To propose and validate a Deep Learning framework for Stock Market Sentiment Analysis.
  • To investigate the efficacy of Deep Learning in processing Big Data for financial insights.

Main Methods:

  • Development and implementation of an automatic classification system using Deep Learning.
  • Evaluation of various Deep Learning approaches (Convolution, LSTM) on distinct datasets.
  • Validation of model performance on Stock Market Sentiment Analysis tasks.

Main Results:

  • Convolutional layers excelled with complex data; LSTM layers effectively managed sequential data.
  • Models achieved up to 73% training accuracy and 69% test accuracy.
  • A simulated model demonstrated a 4.4% Return on Investment.

Conclusions:

  • Deep Learning provides an efficient method for processing Big Data in financial contexts.
  • The developed framework shows practical applicability and potential for real-world financial decision support.