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Approval network: a novel approach for sentiment analysis in social networks

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Abstract

The data-centric impetus and the development of online social networks has led to a significant amount of research that is nowadays more flexible in demonstrating several sociological hypotheses, such as the sentiment influence and transfer among users. Most of the works regarding sentiment classification usually consider text as unique source of information, do not taking into account that social networks are actually networked environments. To overcome this limitation, two main sociological theories should be accounted for addressing any sentiment analysis tasks: homophily and constructuralism. In this paper, we propose Approval Network as a novel graph representation to jointly model homophily and constructuralism, which is intended to better represent the contagion on social networks. To show the potentiality of the proposed representation, two novel sentiment analysis models have been proposed. The first one, related to user-level polarity classification, is approached by presenting a semi-supervised framework grounded on a Markov-based probabilistic model. The second task, aimed at simultaneously extracting aspects and sentiment at message level, is addressed by proposing a novel fully unsupervised generative model. The experimental results show that the proposes sentiment analysis models grounded on Approval Networks are able to outperform not only the traditional models where the relationships are disregarded, but also those computational approaches based on traditional friendship connections.

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Notes

  1. 1 For a detailed survey about diffusion models for social networks please refer to [59].

  2. 2 Note that for this reason the Facebook’s ‘Share’ does not belong to approval tools.

  3. 3 Note that ρ T−b l a c k , ρ T−w h i t e and ρ n e i g h are empirically estimated.

  4. 4 https://code.google.com/p/google-api-spelling-java/.

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  1. Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, Viale Sarca, 336 - 20126, Milan, Italy

    E. Fersini, F. A. Pozzi & E. Messina

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  1. E. Fersini
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  2. F. A. Pozzi
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Fersini, E., Pozzi, F.A. & Messina, E. Approval network: a novel approach for sentiment analysis in social networks. World Wide Web 20, 831–854 (2017). https://doi.org/10.1007/s11280-016-0419-8

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