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Analytic System Based on Prediction Analysis of Social Emotions from Users : A Review

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Analytic System Based on Prediction Analysis of Social Emotions from Users : A Review


Vaishnavi Chimote | Prof. Vrushali D. Dharmale

https://doi.org/10.31142/ijtsrd11441



Vaishnavi Chimote | Prof. Vrushali D. Dharmale "Analytic System Based on Prediction Analysis of Social Emotions from Users : A Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3, April 2018, pp.1608-1612, URL: https://www.ijtsrd.com/papers/ijtsrd11441.pdf

Over social media there are lots of symbols are used as compared to text this is an unstructured type of which get considers day by day increase in such symbols is moving the towards the new data prediction determination technique. Due to the rapid development of Web, large numbers of documents assigned by readers’ emotions have been generated through new portals. Comparing to the previous studies which focused on author’s perspective, our research focuses on readers’ emotions invoked by news articles. Our research provides meaningful assistance in social media application such as sentiment retrieval, opinion summarization and election prediction. In this paper, we predict the readers’ emotion of news based on the social opinion network. More specifically, we construct the opinion network based on the semantic distance. The communities in the news network indicate specific events which are related to the emotions. Therefore, the opinion network serves as the lexicon between events and corresponding emotions. We leverage neighbor relationship in network to predict readers’ emotions. As a result, our methods obtain better result than the state-of-the-art methods. Moreover, we developed a growing strategy to prune the network for practical application. The experiment verifies the rationality of the reduction for application. In this paper, we implement social opinion prediction by generating a real-time social opinion network. In more details, first, we train word vectors according to the most recent Wikipedia word corpus. Second, we calculate se-mantic distance between news via word vectors. As a metric between opinions, semantic distance allows us to construct the opinions growing network to describe the dynamical social opinions. Last, we predict follow-up

Affect sensing and analysis, recognition of group emotion, affective text mining, complex network


IJTSRD11441
Volume-2 | Issue-3, April 2018
1608-1612
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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