1887
Volume 49, Issue 1
  • ISSN 1810-7478
  • E-ISSN: 2589-5230

Abstract

Abstract

The past few decades have seen the rapid development of topic modeling. So far, research has been more concerned with determining the ideal number of topics or meaningful topic clustering words than with applying topic modeling techniques to evaluate linguistic theories. This study proposes the Structural Topic Model (STM)-led framework to facilitate the interpretation of topic modeling results and standardize text analysis. STM encompasses various model training mechanisms, thereby requiring systematic designs to properly combine language studies. “Structural” in STM refers to the inclusion of metadata structure. Unlike the corpus-based keyness approach, STM can capture contextual cues and meta-information for the interpretation of topical results. Besides, STM can make cross-corpora comparisons via topical contrast, a challenging task for corpus-driven related models such as the Biterm Topic Model (BTM). Stylistic variations in song lyrics are taken as an illustration to show how to use the suggested framework to delve into the linguistic theory proposed by Pennebaker (2013). The topical model and iterable model in the proposed paradigm can clarify how pronouns affect style distinction. We believe the proposed STM-led framework can shed light on text analysis by conducting a reproducible cross-corpora comparison on short texts.

Available under the CC BY-NC 4.0 license.
Loading

Article metrics loading...

/content/journals/10.1075/consl.22026.wan
2023-05-25
2024-06-22
Loading full text...

Full text loading...

/deliver/fulltext/consl.22026.wan.html?itemId=/content/journals/10.1075/consl.22026.wan&mimeType=html&fmt=ahah

References

  1. Aarts, F. G. A. M.
    1971 On the distribution of noun-phrase types in English clause structure. Lingua26.31:281–293. 10.1016/0024‑3841(71)90013‑1
    https://doi.org/10.1016/0024-3841(71)90013-1 [Google Scholar]
  2. Abuzayed, Abeer, and Hend Al-Khalifa
    2021 BERT for Arabic topic modeling: An experimental study on BERTopic technique. Procedia Computer Science1891:191–194. 10.1016/j.procs.2021.05.096
    https://doi.org/10.1016/j.procs.2021.05.096 [Google Scholar]
  3. Akella, Revanth, and Teng-Sheng Moh
    2019 Mood classification with lyrics and ConvNet. Proceedings of the 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), ed. byM. A. Wani, 511–514. Los Alamitos, CA: IEEE Computer Society. 10.1109/ICMLA.2019.00095
    https://doi.org/10.1109/ICMLA.2019.00095 [Google Scholar]
  4. Angelov, Dimo
    2020Top2vec: Distributed Representations of Topics. RetrievedJanuary 14th, 2023, fromhttps://arxiv.org/abs/2008.09470
    [Google Scholar]
  5. Aranda, Ana M., Kathrin Sele, Helen Etchanchu, Jonne Y. Guyt, and Eero Vaara
    2021 From big data to rich theory: Integrating critical discourse analysis with structural topic modeling. European Management Review181:197–214. 10.1111/emre.12452
    https://doi.org/10.1111/emre.12452 [Google Scholar]
  6. Arifah, Khadijah
    2016 Figurative Language Analysis in Five John Legend’s Song. Doctoral dissertation, Maulana Malik Ibrahim State Islamic University, Malang, Indonesia.
  7. Arora, Sanjeev, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu
    2013 A practical algorithm for topic modeling with provable guarantees. Proceedings of the 30th International Conference on Machine Learning, ed. bySanjoy Dasgupta and David McAllester, 280–288. Atlanta, GA: JMLR.org.
    [Google Scholar]
  8. Baratè, Adriano, Luca A. Ludovico, and Enrica Santucci
    2013 A semantics-driven approach to lyrics segmentation. Proceedings of the 2013 8th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), ed. byRandall Bilof, 73–79. Los Alamitos, CA: IEEE Computer Society. 10.1109/SMAP.2013.15
    https://doi.org/10.1109/SMAP.2013.15 [Google Scholar]
  9. Barradas, Gonçalo T., and Laura S. Sakka
    2021 When words matter: A cross-cultural perspective on lyrics and their relationship to musical emotions. Psychology of Music50.21:650–669.
    [Google Scholar]
  10. Besson, Mireille, Frederique Faita, Isabelle Peretz, A-M. Bonnel, and Jean Requin
    1998 Singing in the brain: Independence of lyrics and tunes. Psychological Science9.61:494–498. 10.1111/1467‑9280.00091
    https://doi.org/10.1111/1467-9280.00091 [Google Scholar]
  11. Bischof, Jonathan, and Edoardo M. Airoldi
    2012 Summarizing topical content with word frequency and exclusivity. Proceedings of the 29th International Conference on Machine Learning (ICML-12), ed. byJohn Langford and Joelle Pineau, 201–208. Madison, WI: Omnipress.
    [Google Scholar]
  12. Blei, David M.
    2012 Probabilistic topic models. Communications of the ACM55.41:77–84. 10.1145/2133806.2133826
    https://doi.org/10.1145/2133806.2133826 [Google Scholar]
  13. Blei, David M., and John D. Lafferty
    2007 A correlated topic model of Science. The Annals of Applied Statistics1.11:17–35. 10.1214/07‑AOAS114
    https://doi.org/10.1214/07-AOAS114 [Google Scholar]
  14. Blei, David M., Andrew Y. Ng, and Michael I. Jordan
    2003 Latent dirichlet allocation. The Journal of Machine Learning Research31:993–1022.
    [Google Scholar]
  15. Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan Boyd-graber, and David M. Blei
    2009 Reading tea leaves: How humans interpret topic models. Advances in Neural Information Processing Systems321:288–296.
    [Google Scholar]
  16. Chen, Stanley F., and Joshua Goodman
    1999 An empirical study of smoothing techniques for language modeling. Computer Speech & Language13.41:359–394. 10.1006/csla.1999.0128
    https://doi.org/10.1006/csla.1999.0128 [Google Scholar]
  17. Chen, Xieling, Di Zou, Gary Cheng, and Haoran Xie
    2020 Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. Computers & Education1511:103855. 10.1016/j.compedu.2020.103855
    https://doi.org/10.1016/j.compedu.2020.103855 [Google Scholar]
  18. Damerau, Fred J.
    1993 Generating and evaluating domain-oriented multi-word terms from texts. Information Processing & Management29.41:433–447. 10.1016/0306‑4573(93)90039‑G
    https://doi.org/10.1016/0306-4573(93)90039-G [Google Scholar]
  19. Devi, Maibam Debina, and Navanath Saharia
    2020 Exploiting topic modelling to classify sentiment from lyrics. Proceedings of the 2nd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020), ed. byArup Bhattacharjee, Samir Kr. Borgohain, Badal Soni, Gyanendra Verma and Xiao-Zhi Gao, 411–423. Singapore: Springer. 10.1007/978‑981‑15‑6318‑8_34
    https://doi.org/10.1007/978-981-15-6318-8_34 [Google Scholar]
  20. Dewi, Erniyanti Nur Fatahhela, Didin Nuruddin Hidayat, and Alek Alek
    2020 Investigating figurative language in “Lose You to Love Me” song lyric. Loquen: English Studies Journal13.11:6–16. 10.32678/loquen.v13i1.2548
    https://doi.org/10.32678/loquen.v13i1.2548 [Google Scholar]
  21. Dunning, Ted
    1993 Accurate methods for the statistics of surprise and coincidence. Computational Linguistics19.11:61–74.
    [Google Scholar]
  22. Ebeling, Régis, Carlos Abel Córdova Sáenz, Jeferson Campos Nobre, and Karin Becker
    2021 The effect of political polarization on social distance stances in the Brazilian COVID-19 scenario. Journal of Information and Data Management12.11:86–108. 10.5753/jidm.2021.1889
    https://doi.org/10.5753/jidm.2021.1889 [Google Scholar]
  23. Eckstein, Lars
    2010Reading Song Lyrics. Leiden: Brill. 10.1163/9789042030367
    https://doi.org/10.1163/9789042030367 [Google Scholar]
  24. Eisenstein, Jacob, Amr Ahmed, and Eric P. Xing
    2011 Sparse additive generative models of text. Proceedings of the 28th International Conference on Machine Learning (ICML-11), ed. byLise Getoor and Tobias Scheffer, 1041–1048. Madison, WI: Omnipress.
    [Google Scholar]
  25. Gabrielatos, Costas
    2018 Keyness analysis: Nature, metrics and techniques. Corpus Approaches to Discourse: A Critical Review, ed. byCharlotte Taylor and Anna Marchi, 225–258. London: Routledge. 10.4324/9781315179346‑11
    https://doi.org/10.4324/9781315179346-11 [Google Scholar]
  26. Grootendorst, Maarten
    2022BERTopic: Neural Topic Modeling with a Class-based TF-IDF Procedure. RetrievedMay 7th, 2022, from https://arxiv.org/abs/2203.05794
    [Google Scholar]
  27. Hofmann, Thomas
    1999 Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ed. byFredric Gey, Marti Hearst and Richard Tong, 50–57. New York, NY: Association for Computing Machinery. 10.1145/312624.312649
    https://doi.org/10.1145/312624.312649 [Google Scholar]
  28. Hong, Liangjie, and Brian D. Davison
    2010 Empirical study of topic modeling in twitter. Proceedings of the 1st Workshop on Social Media Analytics, ed. byPrem Melville, Jure Leskovec and Foster Provost, 80–88. New York, NY: Association for Computing Machinery. 10.1145/1964858.1964870
    https://doi.org/10.1145/1964858.1964870 [Google Scholar]
  29. Hoover, David L.
    2007 Corpus stylistics, stylometry, and the styles of Henry James. Style41.21:174–203.
    [Google Scholar]
  30. Kilgarriff, Adam
    1997 Using word frequency lists to measure corpus homogeneity and similarity between corpora. Proceedings of the 5th ACL Workshop on Very Large Corpora, ed. byJoe Zhou and Kenneth Church, 231–245. Beijing and Hong Kong: Tsinghua University and The Hong Kong University of Science and Technology.
    [Google Scholar]
  31. 2005 Language is never, ever, ever, random. Corpus Linguistics and Linguistic Theory1.21:263–276. 10.1515/cllt.2005.1.2.263
    https://doi.org/10.1515/cllt.2005.1.2.263 [Google Scholar]
  32. Kreyer, Rolf, and Joybrato Mukherjee
    2007 The style of pop song lyrics: A corpus-linguistic pilot study. Anglia. Journal of English Philology125.11:31–58. 10.1515/ANGL.2007.31
    https://doi.org/10.1515/ANGL.2007.31 [Google Scholar]
  33. Laoh, Enrico, Isti Surjandari, and Limisgy Ramadhina Febirautami
    2018 Indonesians’ song lyrics topic modelling using latent dirichlet allocation. Proceedings of the 2018 5th International Conference on Information Science and Control Engineering (ICISCE), ed. byShaozi Li, Ying Dai and Yun Cheng, 270–274. Los Alamitos, CA: IEEE Computer Society. 10.1109/ICISCE.2018.00064
    https://doi.org/10.1109/ICISCE.2018.00064 [Google Scholar]
  34. Leech, Geoffrey, and Roger Fallon
    1992 Computer corpora-what do they tell us about culture?ICAME Journal161:29–50.
    [Google Scholar]
  35. Li, Peng-Hsuan, Tsu-Jui Fu, and Wei-Yun Ma
    2020 Why attention? Analyze BiLSTM deficiency and its remedies in the case of NER. Proceedings of the AAAI Conference on Artificial Intelligence, ed. byVincent Conitzer and Fei Sha, 8236–8244. California, USA: AAAI Press, Palo Alto. 10.1609/aaai.v34i05.6338
    https://doi.org/10.1609/aaai.v34i05.6338 [Google Scholar]
  36. Lindstedt, Nathan C.
    2019 Structural topic modeling for social scientists: A brief case study with social movement studies literature, 2005–2017. Social Currents6.41:307–318. 10.1177/2329496519846505
    https://doi.org/10.1177/2329496519846505 [Google Scholar]
  37. Mimno, David M., and Andrew McCallum
    2008 Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008), ed. byDavid McAllester and Petri Myllymaki, 411–418. Arlington, VA: AUAI Press.
    [Google Scholar]
  38. Mimno, David M., Hanna M. Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum
    2011 Optimizing semantic coherence in topic models. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, ed. byRegina Barzilay and Mark Johnson, 262–272. Edinburgh, Scotland, UK: Association for Computational Linguistics.
    [Google Scholar]
  39. Nahajec, Lisa
    2019 Song lyrics and the disruption of pragmatic processing: An analysis of linguistic negation in 10CC’s ‘I’m Not in Love’. Language and Literature28.11:23–40. 10.1177/0963947019827072
    https://doi.org/10.1177/0963947019827072 [Google Scholar]
  40. Narayan, Ashwin, Bonnie Berger, and Hyunghoon Cho
    2021 Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology391:765–774. 10.1038/s41587‑020‑00801‑7
    https://doi.org/10.1038/s41587-020-00801-7 [Google Scholar]
  41. Newman, David, Youn Noh, Edmund Talley, Sarvnaz Karimi, and Timothy Baldwin
    2010 Evaluating topic models for digital libraries. Proceedings of the 10th Annual Joint Conference on Digital Libraries, ed. byJane Hunter, 215–224. New York, NY: Association for Computing Machinery. 10.1145/1816123.1816156
    https://doi.org/10.1145/1816123.1816156 [Google Scholar]
  42. North, Adrian C., Amanda E. Krause, and David Ritchie
    2020 The relationship between pop music and lyrics: A computerized content analysis of the United Kingdom’s weekly top five singles, 1999–2013. Psychology of Music49.41:735–758.
    [Google Scholar]
  43. Pennebaker, James W.
    2013The Secret Life of Pronouns: What Our Words Say About Us. London: Bloomsbury Publishing.
    [Google Scholar]
  44. Petrie, Keith J., James W. Pennebaker, and Borge Sivertsen
    2008 Things we said today: A linguistic analysis of the Beatles. Psychology of Aesthetics, Creativity, and the Arts2.41:97–202. 10.1037/a0013117
    https://doi.org/10.1037/a0013117 [Google Scholar]
  45. Pettijohn, Terry F., and Donald F. Sacco Jr.
    2009 The language of lyrics: An analysis of popular Billboard songs across conditions of social and economic threat. Journal of Language and Social Psychology28.31:297–311. 10.1177/0261927X09335259
    https://doi.org/10.1177/0261927X09335259 [Google Scholar]
  46. Pojanapunya, Punjaporn, and Richard Watson Todd
    2018 Log-likelihood and odds ratio: Keyness statistics for different purposes of keyword analysis. Corpus Linguistics and Linguistic Theory14.11:133–167. 10.1515/cllt‑2015‑0030
    https://doi.org/10.1515/cllt-2015-0030 [Google Scholar]
  47. Rajpurkar, Pranav, Jian Zhang, Konstantin Lopyrev, and Percy Liang
    2016 SQuAD: 100,000+ questions for machine comprehension of text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), ed. byJian Su, Kevin Duh and Xavier Carreras, 2383–2392. Stroudsburg, PA: Association for Computational Linguistics. 10.18653/v1/D16‑1264
    https://doi.org/10.18653/v1/D16-1264 [Google Scholar]
  48. Rayson, Paul
    2019 Corpus analysis of key words. The Concise Encyclopedia of Applied Linguistics, ed. byCarol Ann Chapelle, 320–326. Oxford: John Wiley & Sons.
    [Google Scholar]
  49. Roberts, Margaret E., Brandon M. Stewart, and Dustin Tingley
    2016 Navigating the local modes of big data: The case of topic models. Computational Social Science: Discovery and Prediction, ed. byR. Michael Alvarez, 51–97. New York: Cambridge University Press. 10.1017/CBO9781316257340.004
    https://doi.org/10.1017/CBO9781316257340.004 [Google Scholar]
  50. 2019 Stm: An R package for structural topic models. Journal of Statistical Software91.21:1–40. 10.18637/jss.v091.i02
    https://doi.org/10.18637/jss.v091.i02 [Google Scholar]
  51. Roberts, Margaret E., Brandon M. Stewart, Dustin Tingley, and Edoardo M. Airoldi
    2013 The structural topic model and applied social science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation41:1–20.
    [Google Scholar]
  52. Roberts, Margaret E., Brandon M. Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, and David G. Rand
    2014 Structural topic models for open-ended survey responses. American Journal of Political Science58.41:1064–1082. 10.1111/ajps.12103
    https://doi.org/10.1111/ajps.12103 [Google Scholar]
  53. Röder, Michael, Andreas Both, and Alexander Hinneburg
    2015 Exploring the space of topic coherence measures. Proceedings of the 8th ACM International Conference on Web Search and Data Mining, ed. byXueqi Cheng and Hang Li, 399–408. New York, NY: Association for Computing Machinery. 10.1145/2684822.2685324
    https://doi.org/10.1145/2684822.2685324 [Google Scholar]
  54. Sasaki, Shoto, Kazuyoshi Yoshii, Tomoyasu Nakano, Masataka Goto, and Shigeo Morishima
    2014 LyricsRadar: A lyrics retrieval system based on latent topics of lyrics. Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014), ed. byHsin-Min Wang, Yi-Hsuan Yang and Jin Ha Lee, 585–590. Taipei: International Society for Music Information Retrieval.
    [Google Scholar]
  55. Schedl, Markus
    2019 Deep learning in music recommendation systems. Frontiers in Applied Mathematics and Statistics51:44. 10.3389/fams.2019.00044
    https://doi.org/10.3389/fams.2019.00044 [Google Scholar]
  56. Schweinberger, Martin, Michael Haugh, and Sam Hames
    2021 Analysing discourse around COVID-19 in the Australian Twittersphere: A real-time corpus-based analysis. Big Data & Society8.11:1–17. 10.1177/20539517211021437
    https://doi.org/10.1177/20539517211021437 [Google Scholar]
  57. Setiawati, Wilya, and Maryani Maryani
    2018 An analysis of figurative language in Taylor Swift’s song lyrics. PROJECT (Professional Journal of English Education)1.31:261–268. 10.22460/project.v1i3.p261‑268
    https://doi.org/10.22460/project.v1i3.p261-268 [Google Scholar]
  58. Shahmohammadi, Hassan, MirHossein Dezfoulian, and Muharram Mansoorizadeh
    2021 Paraphrase detection using LSTM networks and handcrafted features. Multimedia Tools and Applications80.41:6479–6492. 10.1007/s11042‑020‑09996‑y
    https://doi.org/10.1007/s11042-020-09996-y [Google Scholar]
  59. Sharma, Hardik, Shelly Gupta, Yukti Sharma, and Archana Purwar
    2020 A new model for emotion prediction in music. Proceedings of the 2020 6th International Conference on Signal Processing and Communication (ICSC), ed. byJitendra Mohan and Abhinav Gupta, 156–161. Los Alamitos, CA: IEEE Computer Society. 10.1109/ICSC48311.2020.9182745
    https://doi.org/10.1109/ICSC48311.2020.9182745 [Google Scholar]
  60. Snyder, Robin M.
    2015 An introduction to topic modeling as an unsupervised machine learning way to organize text information. Paper presented at theAnnual Meeting of the Association Supporting Computer Users in Education (ASCUE), Myrtle Beach, SC.
    [Google Scholar]
  61. Sophiadi, Angelina
    2014 The song remains the same… or not? A pragmatic approach to the lyrics of rock music. Major Trends in Theoretical and Applied Linguistics, vol.21, ed. byNikolaos Lavidas, Thomaï Alexiou and Areti-Maria Sougari, 125–142. London: De Gruyter Open Poland. 10.2478/9788376560885.p18
    https://doi.org/10.2478/9788376560885.p18 [Google Scholar]
  62. Sterckx, Lucas
    2014 Topic Detection in a Million Songs. Doctoral dissertation, Ghent University, Ghent, Belgium.
  63. Taddy, Matt
    2012 On estimation and selection for topic models. Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, ed. byNeil D. Lawrence and Mark Girolami, 1184–1193. RetrievedMay 27th, 2022, fromhttps://proceedings.mlr.press/v22/taddy12.html
    [Google Scholar]
  64. Tegge, Friederike
    2017 The lexical coverage of popular songs in English language teaching. System671:87–98. 10.1016/j.system.2017.04.016
    https://doi.org/10.1016/j.system.2017.04.016 [Google Scholar]
  65. Trenquier, Henri
    2018 Improving Semantic Quality of Topic Models for Forensic Investigation. Doctoral dissertation, University of Amsterdam, Amsterdam, Netherlands.
  66. Varnum, Michael E. W., Jaimie Arona Krems, Colin Morris, Alexandra Wormley, and Igor Grossmann
    2021 Why are song lyrics becoming simpler? A time series analysis of lyrical complexity in six decades of American popular music. PLOS ONE16.11:0244576. 10.1371/journal.pone.0244576
    https://doi.org/10.1371/journal.pone.0244576 [Google Scholar]
  67. Wallach, Hanna Megan
    2006 Topic modeling: beyond bag-of-words. Proceedings of the 23rd International Conference on Machine learning, ed. byWilliam W. Cohen and Andrew Moore, 977–984. New York, NY: Association for Computing Machinery. 10.1145/1143844.1143967
    https://doi.org/10.1145/1143844.1143967 [Google Scholar]
  68. 2008 Structured Topic Models for Language. Doctoral dissertation, University of Cambridge, Cambridge, UK.
  69. Wallach, Hanna Megan, Iain Murray, Ruslan Salakhutdinov, and David Mimno
    2009 Evaluation methods for topic models. Proceedings of the 26th Annual International Conference on Machine Learning, ed. byAndrea Danyluk, 1105–1112. New York, NY: Association for Computing Machinery. 10.1145/1553374.1553515
    https://doi.org/10.1145/1553374.1553515 [Google Scholar]
  70. Wang, Jie, and Xinyan Zhao
    2019Theme-Aware Generation Model for Chinese Lyrics. RetrievedSeptember 20th, 2022, fromhttps://arxiv.org/abs/1906.02134
    [Google Scholar]
  71. Watanabe, Kento, Matsubayashi Yuichiroh, Inui Kentaro, Nakano Tomoyasu, Fukayama Satoru, and Goto Masataka
    2017 Lyrisys: An interactive support system for writing lyrics based on topic transition. Proceedings of the 22nd International Conference on Intelligent User Interfaces, ed. byGeorge A. Papadopoulos and Tsvi Kuflik, 559–563. New York, NY: Association for Computing Machinery. 10.1145/3025171.3025194
    https://doi.org/10.1145/3025171.3025194 [Google Scholar]
  72. Weng, Jianshu, Ee-Peng Lim, Jing Jiang, and Qi He
    2010 Twitterrank: Finding topic-sensitive influential twitterers. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, ed. byBrian D. Davison and Torsten Suel, 261–270. New York, NY: Association for Computing Machinery. 10.1145/1718487.1718520
    https://doi.org/10.1145/1718487.1718520 [Google Scholar]
  73. Werner, Valentin
    2021 Catchy and conversational? A register analysis of pop lyrics. Corpora16.21:237–270. 10.3366/cor.2021.0219
    https://doi.org/10.3366/cor.2021.0219 [Google Scholar]
  74. Whissell, Cynthia
    1996 Traditional and emotional stylometric analysis of the songs of Beatles Paul McCartney and John Lennon. Computers and the Humanities30.31:257–265. 10.1007/BF00055109
    https://doi.org/10.1007/BF00055109 [Google Scholar]
  75. Wright, David
    2014 Stylistics Versus Statistics: A Corpus Linguistic Approach to Combining Techniques in Forensic Authorship Analysis Using Enron Emails. Doctoral dissertation, University of Leeds, Leeds, England.
  76. Xia, Xiaoling, Xin Gu, and Qinyang Lu
    2019 Research on the model of lyric emotion algorithm. Journal of Physics: Conference Series12131:042004. 10.1088/1742‑6596/1213/4/042004
    https://doi.org/10.1088/1742-6596/1213/4/042004 [Google Scholar]
  77. Yan, Xiaohui, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng
    2013 A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web, ed. byDaniel Schwabe, Virgílio Almeida and Hartmut Glaser, 1445–1456. New York, NY: Association for Computing Machinery. 10.1145/2488388.2488514
    https://doi.org/10.1145/2488388.2488514 [Google Scholar]
  78. Yao, Liang, Chengsheng Mao, and Yuan Luo
    2019 Graph convolutional networks for text classification. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), ed. byThe Association for the Advancement of Artificial Intelligence, 7370–7377. Palo Alto, CA: AAAI Press. 10.1609/aaai.v33i01.33017370
    https://doi.org/10.1609/aaai.v33i01.33017370 [Google Scholar]
  79. Zhang, Lei, Shuai Wang, and Bing Liu
    2018 Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery8.41:1253. 10.1002/widm.1253
    https://doi.org/10.1002/widm.1253 [Google Scholar]
  80. Zhang, Liang, Keli Xiao, Hengshu Zhu, Chuanren Liu, Jingyuan Yang, and Bo Jin
    2018 CADEN: A context-aware deep embedding network for financial opinions mining. Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), ed. byLisa O’Conner, 757–766. Los Alamitos, CA: IEEE Computer Society. 10.1109/ICDM.2018.00091
    https://doi.org/10.1109/ICDM.2018.00091 [Google Scholar]
  81. Zhao, Wayne Xin, Jing Jiang, Jianshu Weng, Jing He, Ee-Peng Lim, Hongfei Yan, and Xiaoming Li
    2011 Comparing twitter and traditional media using topic models. Advances in Information Retrieval: Proceedings of the 33rd European Conference on IR Research, ed. byPaul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee and Vanessa Mudoch, 338–349. Heidelberg: Springer Berlin. 10.1007/978‑3‑642‑20161‑5_34
    https://doi.org/10.1007/978-3-642-20161-5_34 [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1075/consl.22026.wan
Loading
/content/journals/10.1075/consl.22026.wan
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error