Approval network: a novel approach for sentiment analysis in social networks
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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fersini, E. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2016 |
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| Übergeordnetes Werk: |
Enthalten in: World wide web - Springer US, 1998, 20(2016), 4 vom: 11. Okt., Seite 831-854 |
|---|---|
| Übergeordnetes Werk: |
volume:20 ; year:2016 ; number:4 ; day:11 ; month:10 ; pages:831-854 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11280-016-0419-8 |
|---|
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OLC2062249063 |
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| 520 | |a 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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10.1007/s11280-016-0419-8 doi (DE-627)OLC2062249063 (DE-He213)s11280-016-0419-8-p DE-627 ger DE-627 rakwb eng 004 VZ 24,1 ssgn 54.84$jWebmanagement bkl 06.74$jInformationssysteme bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2016 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. Approval networks User-level sentiment analysis Aspect-level sentiment analysis Social networks Pozzi, F. A. aut Messina, E. aut Enthalten in World wide web Springer US, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)301184976 (DE-600)1485096-5 (DE-576)9301184974 1386-145X nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_70 54.84$jWebmanagement VZ 475288947 (DE-625)475288947 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 AR 20 2016 4 11 10 831-854 |
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Approval network: a novel approach for sentiment analysis in social networks |
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Approval network: a novel approach for sentiment analysis in social networks |
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Fersini, E. |
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approval network: a novel approach for sentiment analysis in social networks |
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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. © Springer Science+Business Media New York 2016 |
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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. © Springer Science+Business Media New York 2016 |
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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. © Springer Science+Business Media New York 2016 |
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