Abstract
We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. All companies produce data from sales, sensors, and various other sources. The main challenges are how can we extract insights from such a rich data environment and if Deep Learning is capable of circumventing Big Data’s challenges. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The main objective of this paper is to develop a computational study and analyze its performance. Deep Learning was able to handle some challenges of Big Data, allowing results to be obtained and compared with real world situations. The outputs contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment Analysis.
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Correia, F., Madureira, A., Bernardino, J. (2022). Deep Learning for Big Data. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_20
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DOI: https://doi.org/10.1007/978-3-030-96299-9_20
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