SciELO - Scientific Electronic Library Online

 
vol.21 issue4Parsing Arabic Nominal Sentences with Transducers to Annotate CorporaSNEIT: Salient Named Entity Identification in Tweets author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Abstract

SATAPATHY, Ranjan et al. Subjectivity Detection in Nuclear Energy Tweets. Comp. y Sist. [online]. 2017, vol.21, n.4, pp.657-664. ISSN 1405-5546.  http://dx.doi.org/10.13053/cys-21-4-2783.

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.

Keywords : Subjectivity detection; nuclear energy tweets.

        · text in English     · English ( pdf )