Subjectivity Detection in Nuclear Energy Tweets

Authors

  • Ranjan Satapathy Nanyang Technological University
  • Iti Chaturvedi Nanyang Technological University
  • Erik Cambria Nanyang Technological University
  • Shirley Ho Nanyang Technological University
  • Jin Cheon Na Nanyang Technological University

DOI:

https://doi.org/10.13053/cys-21-4-2783

Keywords:

Nuclear tweets, Deep Learning, Sentiment Analysis, Reinforcement Learning

Abstract

The subjectivity detection is an important binary classification task that aims at distinguishing natural language texts as opinionated (positive or negative) and non-opinionated (neutral). In this paper, we develop and apply recent subjectivity detection techniques to determine subjective and objective tweets towards the hot topic of nuclear energy. This will further help us to detect the presence or absence of social media bias towards Nuclear Energy. In particular, significant network motifs of words and concepts were learned in dynamic Gaussian Bayesian networks, while using Twitter as a source of information. We use reinforcement learning to update each weight based on a probabilistic reward function over all the weights and, hence, to regularize the sentence model. The proposed framework opens new avenues in helping government agencies manage online public opinion to decide and act according to the need of the hour.

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Published

2017-12-23

Issue

Vol. 21 No. 4 (2017): Advances in Human Language Technologies (Guest Editor: A. Gelbukh)

Section

Articles of the Thematic Issue

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