The author proposes a topic model tailored to the study of creative documents (e.g., academic papers, movie scripts), which extends Poisson factorization in two ways. First, the creativity literature emphasizes the importance of novelty in creative industries. Accordingly, this article introduces a set of residual topics that represent the portion of each document that is not explained by a combination of common topics. Second, creative documents are typically accompanied by summaries (e.g., abstracts, synopses). Accordingly, the author jointly models the content of creative documents and their summaries, and captures systematic variations in topic intensities between the documents and their summaries. This article validates and illustrates the model in three domains: marketing academic papers, movie scripts, and TV show closed captions. It illustrates how the joint modeling of documents and summaries provides some insight into how people summarize creative documents and enhances understanding of the significance of each topic. It shows that the model described produces new measures of distinctiveness that can inform the perennial debate on the relation between novelty and success in creative industries. Finally, the author shows how the proposed model may form the basis for decision support tools that assist people in writing summaries of creative documents.
AskinNoahMauskapfMichael (2017), “What Makes Popular Culture Popular? Product Features and Optimal Differentiation in Music,” American Sociological Review, 820 (5), 910–44.
3.
BergerJonahPackardGrant (2018), “Are Atypical Things More Popular?” Psychological Science, 290 (7), 1178–84.
4.
BielbyWilliam T.BielbyDenise D. (1994), “‘All Hits are Flukes’: Institutionalized Decision Making and the Rhetoric of Network Prime-Time Program Development,” American Journal of Sociology, 990 (5), 1287–313.
5.
BleiDavid M.LaffertyJohn D. (2006), “Dynamic Topic Models,” in Proceedings of the 23rd International Conference on Machine Learning. New York: Association for Computing Machinery, 113–20.
6.
BleiDavid M.LaffertyJohn D. (2009), “Topic Models,” Text Mining: Classification, Clustering, and Applications, 100 (71), 34.
7.
BleiDavid M.KucukelbirAlpMcAuliffeJon D. (2016), “Variational Inference: A Review for Statisticians,” arXiv preprint arXiv:1601.00670.
BoghratiReihaneBergerJonahPackardGrant (2020), “How Writing Style Shapes the Impact of Scientific Findings,” working paper, The Wharton School, University of Pennsylvania.
10.
BüschkenJoachimAllenbyGreg M (2016), “Sentence-Based Text Analysis for Customer Reviews,” Marketing Science, 350 (6), 953–75.
11.
CannyJohn (2004), “Gap: A Factor Model for Discrete Data,” in Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 122–29..
12.
Danescu-Niculescu-MizilCristianChengJustinKleinbergJonLeeLillian (2012), “You Had Me at Hello: How Phrasing Affects Memorability,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, Volume 1. Stroudsburg, PA: Association for Computational Linguistics, 892–901.
13.
DauméHalIIIMarcuDaniel (2006), “Bayesian Query-Focused Summarization,” in Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 305–12.
14.
DelortJean-YvesAlfonsecaEnrique (2012), “Dualsum: A Topic-Model Based Approach for Update Summarization,” in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 214–23.
15.
EliashbergJoshuaHuiSam K.ZhangJohn Z. (2007), “From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts,” Management Science, 530 (6), 881–93.
16.
EliashbergJoshuaHuiSam K.John ZhangZ. (2014), “Assessing Box Office Performance using Movie Scripts: A Kernel-Based Approach,” IEEE Transactions on Knowledge and Data Engineering, 260 (11), 2639–48.
17.
FinkeRonald A.WardThomas B.SmithSteven M. (1992), Creative Cognition: Theory, Research, and Applications. Cambridge, MA: The MIT Press.
18.
FloridaRichard (2014), The Rise of the Creative Class—Revisited: Revised and Expanded. New York: Basic Books.
19.
GioraRachel (2003), On Our Mind: Salience, Context, and Figurative Language. Oxford, UK: Oxford University Press.
20.
GopalanPrem K.CharlinLaurentBleiDavid (2014), “Content-Based Recommendations with Poisson Factorization,” in Advances in Neural Information Processing Systems. New York: Association for Computing Machinery, 3176–84.
21.
GopalanPremHofmanJake M.BleiDavid M. (2013), “Scalable Recommendation with Poisson Factorization,” arXiv preprint arXiv:1311.1704.
22.
HaghighiAriaVanderwendeLucy (2009), “Exploring Content Models for Multi-Document Summarization,” in Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA: Association for Computational Linguistics, 362–70.
23.
HahnMatthew W.Alexander BentleyR. (2003), “Drift as a Mechanism for Cultural Change: An Example from Baby Names,” Proceedings of the Royal Society of London B: Biological Sciences, 2700 (Suppl 1), S120–23.
24.
LiuJiaToubiaOlivier (2018), “A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries,” Marketing Science, 37 (6), 855–1052.
25.
McKeownKathleenRadevDragomir R. (1995), “Generating Summaries of Multiple News Articles,” in Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: Association for Computing Machinery, 74–82.
26.
MednickSarnoff (1962), “The Associative Basis of the Creative Process,” Psychological Review, 690 (3), 220.
27.
MikolovTomasChenKaiCorradoGregDeanJeffrey (2013), “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint arXiv:1301.3781.
28.
MikolovTomasGraveEdouardBojanowskiPiotrPuhrschChristianJoulinArmand (2017), “Advances in Pre-Training Distributed Word Representations,” arXiv preprint arXiv:1712.09405.
29.
NenkovaAniMcKeownKathleen (2012), “A Survey of Text Summarization Techniques,” in Mining Text Data. Berlin/Heidelberg: Springer, 43–76.
PuranamDineshNarayanVishalKadiyaliVrinda (2017), “The Effect of Calorie Posting Regulation on Consumer Opinion: A Flexible Latent Dirichlet Allocation Model with Informative Priors,” Marketing Science, 360 (5), 726–46.
32.
RadevDragomir R.HovyEduardMcKeownKathleen (2002), “Introduction to the Special Issue on Summarization,” Computational Linguistics, 280 (4), 399–408.
33.
RadevDragomir R.McKeownKathleen R. (1998), “Generating Natural Language Summaries from Multiple On-line Sources,” Computational Linguistics, 240 (3), 470–500.
34.
SalganikMatthew J.DoddsPeter SheridanWattsDuncan J. (2006), “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market,” Science, 3110 (5762), 854–56.
StremerschStefanCamachoNunoVannesteSofieVerniersIsabel (2015), “Unraveling Scientific Impact: Citation Types in Marketing Journals,” International Journal of Research in Marketing, 320 (1), 64–77.
38.
StremerschStefanVerniersIsabelVerhoefPeter C. (2007), “The Quest for Citations: Drivers of Article Impact,” Journal of Marketing, 710 (3), 171–93.
39.
TirunillaiSeshadriTellisGerard J. (2014), “Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation,” Journal of Marketing Research, 51 (4), 463–79.
40.
ToubiaOlivierIyengarGarudBunnellRenéeLemaireAlain (2018), “Extracting Features of Entertainment Products: A Guided LDA Approach Informed by the Psychology of Media Consumption,” Journal of Marketing Research, 56 (1), 18–36.
WardThomas B. (1995), “What’s Old About New Ideas,” in The Creative Cognition Approach, SmithS. M.WardT. B.FinkeR. A., eds. Cambridge, MA: The MIT Press, 157–78.
44.
YaoJin-geWanXiaojunXiaoJianguo (2017), “Recent Advances in Document Summarization,” Knowledge and Information Systems, 530 (2), 297–336.
45.
ZhongNingSchweidelDavid A. (2020), “Capturing Changes in Social Media Content: A Multiple Latent Changepoint Topic Model,” Marketing Science, 39 (4), 827–46.
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