Mining association between different emotion classes present in users posts of social media
Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on divers...
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| Autor*in: |
Naznin, Farha [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Social network analysis and mining - Springer Vienna, 2011, 14(2024), 1 vom: 01. Apr. |
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| Übergeordnetes Werk: |
volume:14 ; year:2024 ; number:1 ; day:01 ; month:04 |
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| DOI / URN: |
10.1007/s13278-024-01241-w |
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| Katalog-ID: |
SPR055375278 |
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| 520 | |a Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. | ||
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10.1007/s13278-024-01241-w doi (DE-627)SPR055375278 (SPR)s13278-024-01241-w-e DE-627 ger DE-627 rakwb eng 004 VZ Naznin, Farha verfasserin aut Mining association between different emotion classes present in users posts of social media 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. Sentiment analysis (dpeaa)DE-He213 Emotion analysis (dpeaa)DE-He213 Social media data mining (dpeaa)DE-He213 Fuzzy association rule (dpeaa)DE-He213 Hazarika, Irani aut Laskar, Dwipen aut Mahanta, Anjana Kakoti aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 01. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1007/s13278-024-01241-w lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 AR 14 2024 1 01 04 |
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10.1007/s13278-024-01241-w doi (DE-627)SPR055375278 (SPR)s13278-024-01241-w-e DE-627 ger DE-627 rakwb eng 004 VZ Naznin, Farha verfasserin aut Mining association between different emotion classes present in users posts of social media 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. Sentiment analysis (dpeaa)DE-He213 Emotion analysis (dpeaa)DE-He213 Social media data mining (dpeaa)DE-He213 Fuzzy association rule (dpeaa)DE-He213 Hazarika, Irani aut Laskar, Dwipen aut Mahanta, Anjana Kakoti aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 01. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1007/s13278-024-01241-w lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 AR 14 2024 1 01 04 |
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10.1007/s13278-024-01241-w doi (DE-627)SPR055375278 (SPR)s13278-024-01241-w-e DE-627 ger DE-627 rakwb eng 004 VZ Naznin, Farha verfasserin aut Mining association between different emotion classes present in users posts of social media 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. Sentiment analysis (dpeaa)DE-He213 Emotion analysis (dpeaa)DE-He213 Social media data mining (dpeaa)DE-He213 Fuzzy association rule (dpeaa)DE-He213 Hazarika, Irani aut Laskar, Dwipen aut Mahanta, Anjana Kakoti aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 01. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1007/s13278-024-01241-w lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 AR 14 2024 1 01 04 |
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10.1007/s13278-024-01241-w doi (DE-627)SPR055375278 (SPR)s13278-024-01241-w-e DE-627 ger DE-627 rakwb eng 004 VZ Naznin, Farha verfasserin aut Mining association between different emotion classes present in users posts of social media 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. Sentiment analysis (dpeaa)DE-He213 Emotion analysis (dpeaa)DE-He213 Social media data mining (dpeaa)DE-He213 Fuzzy association rule (dpeaa)DE-He213 Hazarika, Irani aut Laskar, Dwipen aut Mahanta, Anjana Kakoti aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 01. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1007/s13278-024-01241-w lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 AR 14 2024 1 01 04 |
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10.1007/s13278-024-01241-w doi (DE-627)SPR055375278 (SPR)s13278-024-01241-w-e DE-627 ger DE-627 rakwb eng 004 VZ Naznin, Farha verfasserin aut Mining association between different emotion classes present in users posts of social media 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. Sentiment analysis (dpeaa)DE-He213 Emotion analysis (dpeaa)DE-He213 Social media data mining (dpeaa)DE-He213 Fuzzy association rule (dpeaa)DE-He213 Hazarika, Irani aut Laskar, Dwipen aut Mahanta, Anjana Kakoti aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 01. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1007/s13278-024-01241-w lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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 AR 14 2024 1 01 04 |
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mining association between different emotion classes present in users posts of social media |
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Mining association between different emotion classes present in users posts of social media |
| abstract |
Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Emotion analysis is a specialized aspect of sentiment analysis that involves assessing and comprehending the emotions conveyed within textual data. This analytical process, applied to users’ emotions, serves various practical purposes, including healthcare, public opinion analysis on diverse subjects, criminal detection, and personalized recommendations. In this work, two distinct approaches have been presented-a quantitative approach and a fuzzy approach-to extract meaningful rules that reveal associations between various emotion classes present in users’ posts on social media platforms. The proposed methodology is evaluated using a dataset of Twitter users, categorizing emotions according to Ekman’s six classes. Several experiments have been conducted, considering different minimum support and minimum confidence thresholds. Promising results are obtained, as both methods effectively identify associations among different emotion classes present in users’ tweets. Identifying such associations can provide valuable insights, such as how emotion words present from any emotion class determines the presence of emotion words from other emotion class in the tweets of a user. Such findings will definitely be useful for the psychologist to study the emotion psychology of people and in the study of personality of people. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Mining association between different emotion classes present in users posts of social media |
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https://dx.doi.org/10.1007/s13278-024-01241-w |
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Hazarika, Irani Laskar, Dwipen Mahanta, Anjana Kakoti |
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Hazarika, Irani Laskar, Dwipen Mahanta, Anjana Kakoti |
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10.1007/s13278-024-01241-w |
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2024-07-03T15:13:21.351Z |
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|
| score |
7.403097 |