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Media sosial mempunyai peranan yang teramat penting dalam dunia teknologi informasi. Aktivitas yang dilakukan pada media sosial dapat menggambarkan seseorang dalam kondisi nyata, tidak jarang pula pengguna media sosial mencurahkan perasaan atau suasana hatinya pada suatu media sosial yang membuat media sosial dapat diukur, salah satunya pengenalan emosi pada teks media sosial. Model pengenalan emosi yang dihasilkan pada penelitian ini digunakan untuk mengenali emosi pada teks dengan tahapan pengumpulan data teks, praproses teks, pemilihan dan ekstraksi fitur, dan klasifikasi emosi. Model ini diharapkan dapat menjadikan baseline dalam riset yang berkenaan dengan klasifikasi teks pada media sosial, khususnya dalam mengenali emosi pada teks media sosial.
Rincian Artikel
Cara Mengutip
[1]
Aldy Rialdy Atmadja, “Studi Pengembangan Model Pengenalan Emosi Pada Teks Media Sosial”, Jurnal Algoritma, vol. 14, no. 1, hlm. 101–106, Okt 2017.
References
-
[1] APJII, “Penetrasi & Perilaku Pengguna Internet Indonesia 2017,” Asos. Penyelenggara Jasa
Internet Indones., pp. 1”“39, 2017.
[2] “Asia Internet Use, Population Data and 2017, Facebook Statistics - December.” [Online].
Available: https://www.internetworldstats.com/stats3.htm.
[3] B. Subaeki, F. Gunawan, and A. R. Atmadja, “Penggunaan Metode Fuzzy Logic untuk
Pemantauan Sentimen Brand pada Media Sosial,” vol. 1, no. October, pp. 56”“62, 2017.
[4] A. R. Naradhipa and A. Purwarianti, “Sentiment Classification for Indonesian Messages in
Social Media,” Int. Conf. Electr. Eng. Informatics, no. July, pp. 2”“5, 2011.
[5] I. Sunni and D. H. Widyantoro, “Analisis Sentimen dan Ekstraksi Topik Penentu Sentimen
pada Opini Terhadap Tokoh Publik,” J. Sarj. Inst. Teknol. Bandung Bid. Tek. Elektro dan
Inform., vol. 1, no. 2, pp. 200”“206, 2012.
[6] A. R. Atmadja and A. Purwarianti, “Comparison on the rule based method and statistical based
method on emotion classification for Indonesian Twitter text,” 2015 Int. Conf. Inf. Technol.
Syst. Innov. ICITSI 2015 - Proc., 2016.
[7] P. Ekman, “An Argument for Basic Emotions,” Cogn. Emot., vol. 6, no. 3”“4, pp. 169”“200,
1992.
[8] C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” Proc. 2008 ACM
Symp. Appl. Comput. - SAC ”™08, p. 1556, 2008.
[9] A. Valitutti, C. Strapparava, and O. Stock, “Developing affective lexical resources,”
PsychNology J., vol. 2, no. 1, pp. 61”“83, 2004.
[10] W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth, “Harnessing Twitter ”˜Big Data”™ for
Automatic Emotion Identification,” SocialCom/PASSAT, 2012.
[11] R. Burget, J. Karásek, and Z. Smékal, “Recognition of emotions in Czech newspaper
headlines,” Radioengineering, vol. 20, no. 1, pp. 39”“47, 2011.
[12] R. Tokuhisa, K. Inui, and Y. Matsumoto, “Emotion classification using massive examples
extracted from the web,” Proc. 22nd Int. Conf. Comput. Linguist. - COLING ”™08, vol. 1, no.
August, pp. 881”“888, 2008.
[13] P. Ekman, “Are There Basic Emotions?,” Psychological Review, vol. 99, no. 3. pp. 550”“553,
1992.
[14] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, “User study on AffectIM, an avatar-based
Instant Messaging system employing rule-based affect sensing from text,” Int. J. Hum.
Comput. Stud., vol. 68, no. 7, pp. 432”“450, Jul. 2010.
[15] S. Aman, “Recognizing emotions in text,” Www-Scf.Usc.Edu, 2007.
[16] L. Sofiyana, Z. Abidin, and H. Nurhayati, “Klasifikasi Emosi Untuk Teks Berbahasa Indonesia
Dengan Menggunakan K-Nearest Neighbor.” 2012.
[17] O. Nurdiana, Jumadi, and D. Nursantika, “Perbandingan Metode Cosine Similarity Dengan
Metode Jaccard Similarity Pada Aplikasi Pencarian Terjemah Al-Qur”™an Dalam Bahasa
Indonesia,” J. Online Inform., vol. 1, no. 1, pp. 59”“63, 2016.
[18] S. Mac Kim, “Recognising Emotions and Sentiments in Text,” University of Sydney, 2011.
[19] W. B. Zulfikar and N. Lukman, “Perbandingan Naive Bayes Classifier dengan Nearest
Neighbor untuk Identifikasi Penyakit Mata,” JOIN (Jurnal Online Inform., vol. 1, no. 2, pp.
82”“86, 2016.
[20] N. W. S. Saraswati, “Text mining dengan metode naïve bayes classifier dan support vector
machines untuk sentiment analysis,” 2011.
References
[1] APJII, “Penetrasi & Perilaku Pengguna Internet Indonesia 2017,” Asos. Penyelenggara Jasa
Internet Indones., pp. 1”“39, 2017.
[2] “Asia Internet Use, Population Data and 2017, Facebook Statistics - December.” [Online].
Available: https://www.internetworldstats.com/stats3.htm.
[3] B. Subaeki, F. Gunawan, and A. R. Atmadja, “Penggunaan Metode Fuzzy Logic untuk
Pemantauan Sentimen Brand pada Media Sosial,” vol. 1, no. October, pp. 56”“62, 2017.
[4] A. R. Naradhipa and A. Purwarianti, “Sentiment Classification for Indonesian Messages in
Social Media,” Int. Conf. Electr. Eng. Informatics, no. July, pp. 2”“5, 2011.
[5] I. Sunni and D. H. Widyantoro, “Analisis Sentimen dan Ekstraksi Topik Penentu Sentimen
pada Opini Terhadap Tokoh Publik,” J. Sarj. Inst. Teknol. Bandung Bid. Tek. Elektro dan
Inform., vol. 1, no. 2, pp. 200”“206, 2012.
[6] A. R. Atmadja and A. Purwarianti, “Comparison on the rule based method and statistical based
method on emotion classification for Indonesian Twitter text,” 2015 Int. Conf. Inf. Technol.
Syst. Innov. ICITSI 2015 - Proc., 2016.
[7] P. Ekman, “An Argument for Basic Emotions,” Cogn. Emot., vol. 6, no. 3”“4, pp. 169”“200,
1992.
[8] C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” Proc. 2008 ACM
Symp. Appl. Comput. - SAC ”™08, p. 1556, 2008.
[9] A. Valitutti, C. Strapparava, and O. Stock, “Developing affective lexical resources,”
PsychNology J., vol. 2, no. 1, pp. 61”“83, 2004.
[10] W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth, “Harnessing Twitter ”˜Big Data”™ for
Automatic Emotion Identification,” SocialCom/PASSAT, 2012.
[11] R. Burget, J. Karásek, and Z. Smékal, “Recognition of emotions in Czech newspaper
headlines,” Radioengineering, vol. 20, no. 1, pp. 39”“47, 2011.
[12] R. Tokuhisa, K. Inui, and Y. Matsumoto, “Emotion classification using massive examples
extracted from the web,” Proc. 22nd Int. Conf. Comput. Linguist. - COLING ”™08, vol. 1, no.
August, pp. 881”“888, 2008.
[13] P. Ekman, “Are There Basic Emotions?,” Psychological Review, vol. 99, no. 3. pp. 550”“553,
1992.
[14] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, “User study on AffectIM, an avatar-based
Instant Messaging system employing rule-based affect sensing from text,” Int. J. Hum.
Comput. Stud., vol. 68, no. 7, pp. 432”“450, Jul. 2010.
[15] S. Aman, “Recognizing emotions in text,” Www-Scf.Usc.Edu, 2007.
[16] L. Sofiyana, Z. Abidin, and H. Nurhayati, “Klasifikasi Emosi Untuk Teks Berbahasa Indonesia
Dengan Menggunakan K-Nearest Neighbor.” 2012.
[17] O. Nurdiana, Jumadi, and D. Nursantika, “Perbandingan Metode Cosine Similarity Dengan
Metode Jaccard Similarity Pada Aplikasi Pencarian Terjemah Al-Qur”™an Dalam Bahasa
Indonesia,” J. Online Inform., vol. 1, no. 1, pp. 59”“63, 2016.
[18] S. Mac Kim, “Recognising Emotions and Sentiments in Text,” University of Sydney, 2011.
[19] W. B. Zulfikar and N. Lukman, “Perbandingan Naive Bayes Classifier dengan Nearest
Neighbor untuk Identifikasi Penyakit Mata,” JOIN (Jurnal Online Inform., vol. 1, no. 2, pp.
82”“86, 2016.
[20] N. W. S. Saraswati, “Text mining dengan metode naïve bayes classifier dan support vector
machines untuk sentiment analysis,” 2011.
Internet Indones., pp. 1”“39, 2017.
[2] “Asia Internet Use, Population Data and 2017, Facebook Statistics - December.” [Online].
Available: https://www.internetworldstats.com/stats3.htm.
[3] B. Subaeki, F. Gunawan, and A. R. Atmadja, “Penggunaan Metode Fuzzy Logic untuk
Pemantauan Sentimen Brand pada Media Sosial,” vol. 1, no. October, pp. 56”“62, 2017.
[4] A. R. Naradhipa and A. Purwarianti, “Sentiment Classification for Indonesian Messages in
Social Media,” Int. Conf. Electr. Eng. Informatics, no. July, pp. 2”“5, 2011.
[5] I. Sunni and D. H. Widyantoro, “Analisis Sentimen dan Ekstraksi Topik Penentu Sentimen
pada Opini Terhadap Tokoh Publik,” J. Sarj. Inst. Teknol. Bandung Bid. Tek. Elektro dan
Inform., vol. 1, no. 2, pp. 200”“206, 2012.
[6] A. R. Atmadja and A. Purwarianti, “Comparison on the rule based method and statistical based
method on emotion classification for Indonesian Twitter text,” 2015 Int. Conf. Inf. Technol.
Syst. Innov. ICITSI 2015 - Proc., 2016.
[7] P. Ekman, “An Argument for Basic Emotions,” Cogn. Emot., vol. 6, no. 3”“4, pp. 169”“200,
1992.
[8] C. Strapparava and R. Mihalcea, “Learning to identify emotions in text,” Proc. 2008 ACM
Symp. Appl. Comput. - SAC ”™08, p. 1556, 2008.
[9] A. Valitutti, C. Strapparava, and O. Stock, “Developing affective lexical resources,”
PsychNology J., vol. 2, no. 1, pp. 61”“83, 2004.
[10] W. Wang, L. Chen, K. Thirunarayan, and A. P. Sheth, “Harnessing Twitter ”˜Big Data”™ for
Automatic Emotion Identification,” SocialCom/PASSAT, 2012.
[11] R. Burget, J. Karásek, and Z. Smékal, “Recognition of emotions in Czech newspaper
headlines,” Radioengineering, vol. 20, no. 1, pp. 39”“47, 2011.
[12] R. Tokuhisa, K. Inui, and Y. Matsumoto, “Emotion classification using massive examples
extracted from the web,” Proc. 22nd Int. Conf. Comput. Linguist. - COLING ”™08, vol. 1, no.
August, pp. 881”“888, 2008.
[13] P. Ekman, “Are There Basic Emotions?,” Psychological Review, vol. 99, no. 3. pp. 550”“553,
1992.
[14] A. Neviarouskaya, H. Prendinger, and M. Ishizuka, “User study on AffectIM, an avatar-based
Instant Messaging system employing rule-based affect sensing from text,” Int. J. Hum.
Comput. Stud., vol. 68, no. 7, pp. 432”“450, Jul. 2010.
[15] S. Aman, “Recognizing emotions in text,” Www-Scf.Usc.Edu, 2007.
[16] L. Sofiyana, Z. Abidin, and H. Nurhayati, “Klasifikasi Emosi Untuk Teks Berbahasa Indonesia
Dengan Menggunakan K-Nearest Neighbor.” 2012.
[17] O. Nurdiana, Jumadi, and D. Nursantika, “Perbandingan Metode Cosine Similarity Dengan
Metode Jaccard Similarity Pada Aplikasi Pencarian Terjemah Al-Qur”™an Dalam Bahasa
Indonesia,” J. Online Inform., vol. 1, no. 1, pp. 59”“63, 2016.
[18] S. Mac Kim, “Recognising Emotions and Sentiments in Text,” University of Sydney, 2011.
[19] W. B. Zulfikar and N. Lukman, “Perbandingan Naive Bayes Classifier dengan Nearest
Neighbor untuk Identifikasi Penyakit Mata,” JOIN (Jurnal Online Inform., vol. 1, no. 2, pp.
82”“86, 2016.
[20] N. W. S. Saraswati, “Text mining dengan metode naïve bayes classifier dan support vector
machines untuk sentiment analysis,” 2011.