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...
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Autor*in:	Fersini, E. [verfasserIn] Pozzi, F. A. [verfasserIn] Messina, E. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Approval networks User-level sentiment analysis Aspect-level sentiment analysis Social networks

Übergeordnetes Werk:	Enthalten in: World wide web - Dordrecht [u.a.] : Springer Science + Business Media B.V, 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:	Volltext

DOI / URN:	10.1007/s11280-016-0419-8

Katalog-ID:	SPR018618499

Internformat


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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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matchkey_str	article:15731413:2016----::prvlewraoeapocfretmnaayi
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allfields	10.1007/s11280-016-0419-8 doi (DE-627)SPR018618499 (SPR)s11280-016-0419-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.84 bkl 06.74 bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Pozzi, F. A. verfasserin aut Messina, E. verfasserin aut Enthalten in World wide web Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)319530604 (DE-600)2025142-7 1573-1413 nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://dx.doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.84 ASE 06.74 ASE AR 20 2016 4 11 10 831-854
spelling	10.1007/s11280-016-0419-8 doi (DE-627)SPR018618499 (SPR)s11280-016-0419-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.84 bkl 06.74 bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Pozzi, F. A. verfasserin aut Messina, E. verfasserin aut Enthalten in World wide web Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)319530604 (DE-600)2025142-7 1573-1413 nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://dx.doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.84 ASE 06.74 ASE AR 20 2016 4 11 10 831-854
allfields_unstemmed	10.1007/s11280-016-0419-8 doi (DE-627)SPR018618499 (SPR)s11280-016-0419-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.84 bkl 06.74 bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Pozzi, F. A. verfasserin aut Messina, E. verfasserin aut Enthalten in World wide web Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)319530604 (DE-600)2025142-7 1573-1413 nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://dx.doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.84 ASE 06.74 ASE AR 20 2016 4 11 10 831-854
allfieldsGer	10.1007/s11280-016-0419-8 doi (DE-627)SPR018618499 (SPR)s11280-016-0419-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.84 bkl 06.74 bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Pozzi, F. A. verfasserin aut Messina, E. verfasserin aut Enthalten in World wide web Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)319530604 (DE-600)2025142-7 1573-1413 nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://dx.doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.84 ASE 06.74 ASE AR 20 2016 4 11 10 831-854
allfieldsSound	10.1007/s11280-016-0419-8 doi (DE-627)SPR018618499 (SPR)s11280-016-0419-8-e DE-627 ger DE-627 rakwb eng 004 ASE 54.84 bkl 06.74 bkl Fersini, E. verfasserin aut Approval network: a novel approach for sentiment analysis in social networks 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213 Pozzi, F. A. verfasserin aut Messina, E. verfasserin aut Enthalten in World wide web Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 20(2016), 4 vom: 11. Okt., Seite 831-854 (DE-627)319530604 (DE-600)2025142-7 1573-1413 nnns volume:20 year:2016 number:4 day:11 month:10 pages:831-854 https://dx.doi.org/10.1007/s11280-016-0419-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.84 ASE 06.74 ASE AR 20 2016 4 11 10 831-854
language	English
source	Enthalten in World wide web 20(2016), 4 vom: 11. Okt., Seite 831-854 volume:20 year:2016 number:4 day:11 month:10 pages:831-854
sourceStr	Enthalten in World wide web 20(2016), 4 vom: 11. Okt., Seite 831-854 volume:20 year:2016 number:4 day:11 month:10 pages:831-854
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Approval networks User-level sentiment analysis Aspect-level sentiment analysis Social networks
dewey-raw	004
isfreeaccess_bool	false
container_title	World wide web
authorswithroles_txt_mv	Fersini, E. @@aut@@ Pozzi, F. A. @@aut@@ Messina, E. @@aut@@
publishDateDaySort_date	2016-10-11T00:00:00Z
hierarchy_top_id	319530604
dewey-sort	14
id	SPR018618499
language_de	englisch
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author	Fersini, E.
spellingShingle	Fersini, E. ddc 004 bkl 54.84 bkl 06.74 misc Approval networks misc User-level sentiment analysis misc Aspect-level sentiment analysis misc Social networks Approval network: a novel approach for sentiment analysis in social networks
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topic_title	004 ASE 54.84 bkl 06.74 bkl Approval network: a novel approach for sentiment analysis in social networks Approval networks (dpeaa)DE-He213 User-level sentiment analysis (dpeaa)DE-He213 Aspect-level sentiment analysis (dpeaa)DE-He213 Social networks (dpeaa)DE-He213
topic	ddc 004 bkl 54.84 bkl 06.74 misc Approval networks misc User-level sentiment analysis misc Aspect-level sentiment analysis misc Social networks
topic_unstemmed	ddc 004 bkl 54.84 bkl 06.74 misc Approval networks misc User-level sentiment analysis misc Aspect-level sentiment analysis misc Social networks
topic_browse	ddc 004 bkl 54.84 bkl 06.74 misc Approval networks misc User-level sentiment analysis misc Aspect-level sentiment analysis misc Social networks
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title	Approval network: a novel approach for sentiment analysis in social networks
ctrlnum	(DE-627)SPR018618499 (SPR)s11280-016-0419-8-e
title_full	Approval network: a novel approach for sentiment analysis in social networks
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author_browse	Fersini, E. Pozzi, F. A. Messina, E.
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title_sort	approval network: a novel approach for sentiment analysis in social networks
title_auth	Approval network: a novel approach for sentiment analysis in social networks
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Approval network: a novel approach for sentiment analysis in social networks
url	https://dx.doi.org/10.1007/s11280-016-0419-8
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author2	Pozzi, F. A. Messina, E.
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doi_str	10.1007/s11280-016-0419-8
up_date	2024-07-03T20:59:58.609Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018618499</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111061946.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2016 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11280-016-0419-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018618499</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11280-016-0419-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.84</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fersini, E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Approval network: a novel approach for sentiment analysis in social networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Approval networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">User-level sentiment analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aspect-level sentiment analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Social networks</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pozzi, F. A.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Messina, E.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">World wide web</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998</subfield><subfield code="g">20(2016), 4 vom: 11. 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