Sentiment Analysis Applied to Analyze Society's Emotion in Two Different Context in Social Media

Authors

  • Marilyn Minicucci Ibañnez National Institute for Space Research, Sñao Jos´e dos Campos, Sñao Paulo, Brazil
  • Reinaldo Roberto Rosa National Institute for Space Research, Sñao Jos´e dos Campos, Sñao Paulo, Brazil
  • Lamartine N. F. Guimarães Institute for Advanced Studies, Sñao Jos´e dos Campos, Sñao Paulo, Brazil

DOI:

https://doi.org/10.4114/intartif.vol23iss66pp66-84

Keywords:

Machine Learning, Deep Learning, Auto-encoder, Natural Language Processing, Sentiment Analysis, Social Media

Abstract

In the last few decades, the growth in the use of the Internet has generated a substantial increase in the circulation of information on social media. Due to the high interest of several areas of society in the analysis of these data, a study of better techniques for the manipulation and understanding of this type of data is of great importance so that this enormous volume of information can be interpreted quickly and accurately. Based on this context, this study shows two approaches of sentiment analysis to verify the emotion of the population in different context. The first approach analyses the 2018 presidential elections in Brazil considering data from the Twitter social network. The second approach performs analysis of data from social media to identify threats level of armed conflicts considering data off the conflict between Syria and the USA in 2017. To achieve this goal, machine learning techniques such as auto-encoder and deep learning will be considered in conjunction with NLP text analysis techniques. The results obtained show the effectiveness of the approaches used in the classification of feelings within the domains used according to the methodology developed for this work.

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Published

2020-12-27

How to Cite

Minicucci Ibañnez, M., Roberto Rosa, R., & N. F. Guimarães, L. (2020). Sentiment Analysis Applied to Analyze Society’s Emotion in Two Different Context in Social Media. Inteligencia Artificial, 23(66), 66–84. https://doi.org/10.4114/intartif.vol23iss66pp66-84