Subjectivity detection in nuclear energy tweets

Satapathy, Ranjan, Chaturvedi, Iti, Cambria, Erik, Ho, Shirley S., and Na, Jin Cheon (2017) Subjectivity detection in nuclear energy tweets. Computacion y Sistemas, 21 (4). pp. 657-664.

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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.

Item ID: 63351
Item Type: Article (Research - C1)
ISSN: 1405-5546
Keywords: Nuclear energy tweets, Subjectivity detection
Copyright Information: This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
Funders: Singapore National Research Foundation (NRF)
Projects and Grants: NRF NPRP Award No. NRF2014NPR-NPRP001-004
Date Deposited: 02 Sep 2020 02:22
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing @ 100%
SEO Codes: 85 ENERGY > 8504 Energy Transformation > 850403 Nuclear Energy @ 100%
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