A Model of Text for Experimentation in the Social Sciences

Journal of the American Statistical Association

Volumn 111, Issue 515, 2016, Pages 988-1003

  Roberts, Margaret E ^a   Stewart, Brandon M ^b   Airoldi, Edoardo M ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b PRINCETON UNIVERSITY   (United States)

^c HARVARD UNIVERSITY   (United States)

Author keywords

Causal inference; Experimentation; High dimensional inference; Social sciences; Text analysis; Variational approximation

Indexed keywords

EID: 84978784705     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2016.1141684     Document Type: Article

References (102)

1. A.Ahmed, and E.Xing, (2007), “On Tight Approximate Inference of the Logistic-Normal Topic Admixture Model,” in Proceedings of AISTATS, pp. 19--26.
2. ——— (2010), “Staying Informed:Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective,” in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 1140–1150.
3. E.Airoldi, E.Erosheva, S.Fienberg, C.Joutard, T.Love, and S.Shringarpure, (2010), “Reconceptualizing the Classification of PNAS Articles,” Proceedings of the National Academy of Sciences, 107, 20899–20904.
4. E.M.Airoldi, A.G.Anderson, S.E.Fienberg, and K.K.Skinner, (2006), “Who Wrote Ronald Reagan’s Radio Addresses?” Bayesian Analysis, 1, 289–320.
5. E.M.Airoldi, and J.M.Bischof, (in press), “A Regularization Scheme on Word Occurrence Rates That Improves Estimation and Interpretation of Topical Content” (with discussion), Journal of American Statistical Association.
6. E.M.Airoldi, D.M.Blei, E.A.Erosheva, and S.E.Fienberg, S. E. (eds.) (2014a), Handbook of Mixed Membership Models and Their Applications (Handbooks of Modern Statistical Methods), Boca Raton, FL:Chapman & Hall/CRC.
7. ——— (2014b), “Introduction to Mixed Membership Models and Methods,” in Handbook of Mixed Membership Models and Their Applications (Handbooks of Modern Statistical Methods), Boca Raton, FL:Chapman & Hall/CRC, pp. 3–14.
8. E.M.Airoldi, W.W.Cohen, and S.E.Fienberg, (2005), “Bayesian Models for Frequent Terms in Text,” in Proceedings of the Classification Society of North America and INTERFACE Annual Meetings.
9. E.M.Airoldi, and S.E.Fienberg, (2003), “Who Wrote Ronald Reagan’s Radio Addresses?” Technical Report no. CMU-STAT-03-789, Carnegie Mellon University.
10. J.Aitchison, (1982), “The Statistical Analysis of Compositional Data,” Journal of the Royal Statistical Society, Series B, 44, 139–177.
11. J.Aitchison, and S.M.Shen, (1980), “Logistic-Normal Distributions:Some Properties and Uses,” Biometrika, 67, 261–272.
12. D.Andrzejewski, X.Zhu, M.Craven, and B.Recht, (2011), “A Framework for Incorporating General Domain Knowledge Into Latent Dirichlet Allocation using First-Order Logic,” in IJCAI, pp. 25--32.
13. S.Arora, R.Ge, Y.Halpern, D.Mimno, A.Moitra, D.Sontag, Y.Wu, and M.Zhu, (2013), “A Practical Algorithm for Topic Modeling With Provable Guarantees,” in Proceedings of The 30th International Conference on Machine Learning, pp. 280–288.
14. S.Arora, R.Ge, and A.Moitra, (2012), “Learning Topic Models–Going Beyond Svd,” in IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS), pp. 1–10.
15. A.Asuncion, M.Welling, P.Smyth, and Y.-W.Teh, (2009), “On Smoothing and Inference for Topic Models,” in UAI, pp. 27--34.
16. J.M.Bischof, and E.M.Airoldi, (2012), “Summarizing Topical Content With Word Frequency and Exclusivity,” in International Conference on Machine Learning (Vol. 29), pp. 201--208.
17. D.M.Blei, (2012), “Probabilistic Topic Models,” Communications of the ACM, 55, 77–84.
18. D.M.Blei, and J.D.Lafferty, (2006), “Dynamic Topic Models,” in ICML, 113--120.
19. ——— (2007), “A Correlated Topic Model of Science,” Annals of Applied Statistics, 1, 17–35.
20. ——— (2009), “Topic Models,” in Text Mining:Classification, Clustering, and Applications, eds. A. N. Srivastava and M. Sahami, Boca Raton, FL:CRC Press, pp. 71--94.
21. D.M.Blei, A.Y.Ng, and M.I.Jordan, (2001), “Latent Dirichlet allocation,” in Advances in Neural Information Processing Systems, pp. 601–608.
22. ——— (2003), “Latent Dirichlet Allocation,” Journal of Machine Learning Research, 3, 993–1022.
23. M.Braun, and J.McAuliffe, (2010), “Variational Inference for Large-Scale Models of Discrete Choice,” Journal of the American Statistical Association, 105, 324–335.
24. M.Buot, and D.Richards, (2006), “Counting and Locating the Solutions of Polynomial Systems of Maximum Likelihood Equations, I,” Journal of Symbolic Computation, 41, 234–244.
25. A.Chaney, and D.Blei, (2012), Visualizing Topic Models, Princeton, NJ:Department of Computer Science, Princeton University.
26. J.Chang, J.Boyd-Graber, C.Wang, S.Gerrish, and D.M.Blei, (2009), “Reading Tea Leaves:How Humans Interpret Topic Models,” in NIPS, 288--296.
27. J.Chen, J.Zhu, Z.Wang, X.Zheng, and B.Zhang, (2013), “Scalable Inference for Logistic-Normal Topic Models,” in Advances in Neural Information Processing Systems, pp. 2445–2453.
28. J.Chuang, C.Manning, and J.Heer, (2012a), “Termite:Visualization Techniques for Assessing Textual Topic Models,” in Proceedings of the International Working Conference on Advanced Visual Interfaces, ACM, pp. 74–77.
29. J.Chuang, D.Ramage, C.Manning, and J.Heer, (2012b), “Interpretation and Trust:Designing Model-Driven Visualizations for Text Analysis,” in Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, ACM, pp. 443–452.
30. C.De Boor, C.De Boor, C.De Boor, and C.De Boor, (1978), A Practical Guide to Splines (Vol. 27), New York:Springer-Verlag.
31. A.P.Dempster, N.Laird, and D.B.Rubin, (1977), “Maximum Likelihood Estimation From Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Association, 39, 1–38.
32. G.Doyle, and C.Elkan, (2009), “Accounting for Burstiness in Topic Models,” in ICML, 281--288.
33. J.Eisenstein, A.Ahmed, and E.Xing, (2011), “Sparse Additive Generative Models of Text,” in Proceedings of ICML, pp. 1041–1048.
34. J.Eisenstein, B.O’Connor, N.Smith, and E.Xing, (2010), “A Latent Variable Model for Geographic Lexical Variation,” in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 1277–1287.
35. E.A.Erosheva, S.E.Fienberg, and J.Lafferty, (2004), “Mixed-Membership Models of Scientific Publications,” Proceedings of the National Academy of Sciences, 101, 5220–5227.
36. Y.-J.Fang, (2001), “Reporting the Same Events? A Critical Analysis of Chinese Print News Media Texts,” Discourse & Society, 12, 585–613.
37. N.Ferguson, (2010), “Complexity and Collapse:Empires on the Edge of Chaos,” Foreign Affairs, 89, 18--32.
38. J.Friedman, T.Hastie, and R.Tibshirani, (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of statistical software, 33, 1--22.
39. A.Gelman, and J.Hill, (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models (Vol. 3), New York:Cambridge University Press.
40. A.Gelman, X.-L.Meng, and H.Stern, (1996), “Posterior Predictive Assessment of Model Fitness via Realized Discrepancies,” Statistica Sinica, 6, 733–760.
41. A.Genkin, D.D.Lewis, and D.Madigan, (2007), “Large-Scale Bayesian Logistic Regression for Text Categorization,” Technometrics, 49, 291--304.
42. S.Gerrish, and D.Blei, (2012), “How They Vote:Issue-Adjusted Models of Legislative Behavior,” in Advances in Neural Information Processing Systems, 25, 2762–2770.
43. T.L.Griffiths, and M.Steyvers, (2004), “Finding Scientific Topics,” Proceedings of the National Academy of Sciences of the United States of America, 101, 5228–5235.
44. J.Grimmer, (2010), “A Bayesian Hierarchical Topic Model for Political Texts:Measuring Expressed Agendas in Senate Press Releases,” Political Analysis, 18, 1--35.
45. J.Grimmer, and G.King, (2011), “General Purpose Computer-Assisted Clustering and Conceptualization,” Proceedings of the National Academy of Sciences, 108, 2643–2650.
46. J.Grimmer, and B.M.Stewart, (2013), “Text as Data:The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts,” Political Analysis, 21, 267–297.
47. P.C.Groenewald, and L.Mokgatlhe, (2005), “Bayesian Computation for Logistic Regression,” Computational Statistics & Data Analysis, 48, 857–868.
48. T.Hastie, and R.Tibshirani, (1990), Generalized Additive Models (Vol. 43), Boca Raton, FL:Chapman & Hall/CRC.
49. C.C.Holmes, and L.Held, (2006), “Bayesian Auxiliary Variable Models for Binary and Multinomial Regression,” Bayesian Analysis, 1, 145–168.
50. K.Hornik, (2005), “A CLUE for CLUster Ensembles,” Journal of Statistical Software, 14, 1--25.
51. Y.Hu, J.L.Boyd-Graber, and B.Satinoff, (2011), “Interactive Topic Modelingin,” in ACL, pp. 248–257.
52. J.Hurley, and R.Cattell, (1962), “The Procrustes Program:Producing Direct Rotation to Test a Hypothesized Factor Structure,” Behavioral Science, 7, 258–262.
53. G.J.Ikenberry, (2008), “The Rise of China and the Future of the West:Can the Liberal System Survive?” Foreign Affairs, 87, 23–37.
54. D.R.Jeske, and R.Y.Liu, (2007), “Mining and Tracking Massive Text Data:Classification, Construction of Tracking Statistics, and Inference Under Misclassification,” Technometrics, 49, 116–128.
55. J.Jia, L.Miratrix, B.Yu, B.Gawalt, L.El Ghaoui, L.Barnesmoore, and S.Clavier, (2014), “Concise Comparative Summaries (CCS) of Large Text Corpora With a Human Experiment,” Annals of Applied Statistics, 8, 499–529.
56. A.I.Johnston, and D.Stockmann, (2007), Chinese Attitudes Toward the United States and Americans, Ithaca, NY:Cornell University Press, pp. 157–195.
57. M.E.Khan, and G.Bouchard, (2009), “Variational EM Algorithms for Correlated Topic Models,” Technical Report, University of British Columbia.
58. D.A.Knowles, and T.Minka, (2011), “Non-Conjugate Variational Message Passing for Multinomial and Binary Regression,” in Advances in Neural Information Processing Systems, pp. 1701–1709.
59. J.S.Liu, (1994), “The Collapsed Gibbs Sampler in Bayesian Computations With Applications to a Gene Regulation Problem,” Journal of the American Statistical Association, 89, 958–966.
60. C.Lucas, R.Nielsen, M.E.Roberts, B.M.Stewart, A.Storer, and D.Tingley, (2015), “Computer Assisted Text Analysis for Comparative Politics,” Political Analysis, 23, 254–277.
61. C.D.Manning, P.Raghavan, and H.Schütze, (2008), An Introduction to Information Retrieval, Cambridge, England:Cambridge University Press.
62. Q.Mei, X.Shen, and C.Zhai, (2007), “Automatic Labeling of Multinomial Topic Models,” in KDD, pp. 490–499.
63. T.C.Mendenhall, (1887), “The Characteristic Curves of Composition,” Science, 11, 237–249.
64. X.-L.Meng, and D.Van Dyk, (1997), “The EM Algorithm—An Old Folk-Song Sung to a Fast New Tune,” Journal of the Royal Statistical Society, Series B, 59, 511–567.
65. G.A.Miller, E.B.Newman, and E.A.Friedman, (1958), “Length-Frequency Statistics for Written English,” Information and Control, 1, 370–389.
66. D.Mimno, and D.Blei, (2011), “Bayesian Checking for Topic Models,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 227–237.
67. D.Mimno, and A.McCallum, (2008), “Topic Models Conditioned on Arbitrary Features With Dirichlet-Multinomial Regression,” in UAI, pp. 411–418.
68. D.Mimno, H.Wallach, and A.McCallum, (2008), “Gibbs Sampling for Logistic Normal Topic Models With Graph-Based Priors,” in NIPS Workshop on Analyzing Graphs, pp. 1--8.
69. D.Mimno, H.Wallach, E.Talley, M.Leenders, and A.McCallum, (2011), “Optimizing Semantic Coherence in Topic Models,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, pp. 262–272.
70. T.Minka, and J.D.Lafferty, (2002), “Expectation-Propagation for the Generative Aspect Model,” in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI), pp. 352–359.
71. F.Mosteller, and D.Wallace, (1963), “Inference in an Authorship Problem,” Journal of the American Statistical Association, 58, 275–309.
72. F.Mosteller, and D.L.Wallace, (1964), Inference and Disputed Authorship:The Federalist, Reading, MA:Addison-Wesley.
73. ——— (1984), Applied Bayesian and Classical Inference:The Case of The Federalist Papers, New York:Springer-Verlag.
74. I.Mukherjee, and D.M.Blei, (2009), “Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation,” in Neural Information Processing Systems, pp. 1129--1136.
75. X.Nguyen, (2015), “Posterior Contraction of the Population Polytope in Finite Admixture Models,” Bernoulli, 21, 618--646.
76. J.Paisley, C.Wang, and D.Blei, (2012), “The Discrete Infinite Logistic Normal Distribution,” Bayesian Analysis, 7, 235–272.
77. C.H.Papadimitriou, and K.Steiglitz, (1998), Combinatorial Optimization:Algorithms and Complexity, Mineola, NY:Courier Dover Publications.
78. N.G.Polson, J.G.Scott, and J.Windle, (2013), “Bayesian Inference for Logistic Models Using Polya-Gamma Latent Variables,” Journal of the American Statistical Association, 108, 1339--1349.
79. J.K.Pritchard, M.Stephens, N.A.Rosenberg, and P.Donnelly, (2000), “Association Mapping in Structured Populations,” American Journal of Human Genetics, 67, 170–181.
80. K.Quinn, B.Monroe, M.Colaresi, M.Crespin, and D.Radev, (2010), “How to Analyze Political Attention With Minimal Assumptions and Costs,” American Journal of Political Science, 54, 209–228.
81. D.Ramage, D.Hall, R.Nallapati, and C.D.Manning, (2009), “Labeled LDA:A Supervised Topic Model for Credit Attribution in Multi-Labeled Corpora,” in EMNLP, pp. 248--256.
82. J.Reich, D.Tingley, J.Leder-Luis, M.E.Roberts, and B.M.Stewart, (2015), “Computer Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses,” Journal of Learning Analytics, 2, 156--184.
83. M.E.Roberts, B.M.Stewart, and D.Tingley, (2014a), “stm:R Package for Structural Topic Models,” Technical Report, Harvard University.
84. ——— (2016), “Navigating the Local Modes of Big Data:The Case of Topic Models,” Computational Social Science:Discovery and Prediction, ed. R. M. Alvarez, New York:Cambridge University Press, pp. 51–97.
85. M.E.Roberts, B.M.Stewart, D.Tingley, C.Lucas, J.Leder-Luis, S.Gadarian, B.Albertson, and D.Rand, (2014b), “Structural Topic Models for Open-Ended Survey Responses,” American Journal of Political Science, 58, 1064–1082.
86. P.R.Rosenbaum, (1987), “Model-Based Direct Adjustment,” Journal of the American Statistical Association, 82, 387–394.
87. M.Rosen-Zvi, T.Griffiths, M.Steyvers, and P.Smyth, (2004), “The Author-Topic Model for Authors and Documents,” in UAI, pp. 487--494.
88. D.Roy, (1996), “The ”China Threat” Issue:Major Arguments,” Asian Survey, 36, 758–771.
89. R.Socher, S.Gershman, A.Perotte, P.Sederberg, D.Blei, and K.Norman, (2009), “A Bayesian Analysis of Dynamics in Free Recall,” Advances in Neural Information Processing Systems, 22, 1714–1722.
90. M.Stephens, (2000), “Bayesian Analysis of Mixtures With an Unknown Number of Components—An Alternative to Reversible Jump Methods,” Annals of Statistics, 28, 40–74.
91. M.Taddy, (2013), “Multinomial Inverse Regression for Text Analysis,” Journal of the American Statistical Association, 108, 755–770.
92. ——— (2015), “Distributed Multinomial Regression,” Annals of Applied Statistics, 9, 1394--1414.
93. J.Tang, Z.Meng, X.Nguyen, Q.Mei, and M.Zhang, (2014), “Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis,” Journal of Machine Learning Research, 32, 190–198.
94. P.Toulis, and E.M.Airoldi, (2014), “Implicit Stochastic Gradient Descent,” arxiv no. 1408.2923. Available at https://arxiv.org/abs/1408.2923
95. ——— (2015), “Scalable Estimation Strategies Based on Stochastic Approximations:Classical Results and New Insights,” Statistics and Computing, 25, 781–795.
96. P.D.Turney, and P.Pantel, (2010), “From Frequency to Meaning:Vector Space Models of Semantics,” Journal of Artificial Intelligence Research, 37, 141–188.
97. H.Wallach, D.Mimno, and A.McCallum, (2009a), “Rethinking LDA:Why Priors Matter,” in NIPS, pp. 1973--1981.
98. H.Wallach, I.Murray, R.Salakhutdinov, and D.Mimno, (2009b), “Evaluation Methods for Topic Models,” in ICML, pp. 1105--1112.
99. C.Wang, and D.Blei, (2013), “Variational Inference in Nonconjugate Models,” Journal of Machine Learning Research, 14, 1005–1031.
100. U.Yule, (1944), The Statistical Study of Literary Vocabulary, Cambridge, UK:Cambridge University Press.
101. G.K.Zipf, (1932), Selected Studies of the Principle of Relative Frequency in Language, Cambridge, MA:Harvard University Press.
102. J.Zou, and R.Adams, (2012), “Priors for Diversity in Generative Latent Variable Models,” Advances in Neural Information Processing Systems, 25, 3005–3013.