class: center, middle, inverse, title-slide # Workshop de Topic Modeling ## Slides – <a href="https://storopoli.io/topic-modeling-workshop">.white[storopoli.io/topic-modeling-workshop]</a> ### Jose Storopoli, PhD ### <img src="images/UNINOVE_CIS.png" width="124" /><img src="images/UNINOVE_PPGA.png" width="124" /> ### <a href="http://creativecommons.org/licenses/by-sa/4.0/"><img src="https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg" alt="CC BY-SA 4.0" /></a> ### 04/05/2021 --- class: animated, fadeIn layout: true --- <div> <style type="text/css">.xaringan-extra-logo { width: 110px; height: 55px; z-index: 0; background-image: url(https://raw.githubusercontent.com/storopoli/UNINOVE-xaringan-theme/master/resources/uninove.png); background-size: contain; background-repeat: no-repeat; position: absolute; top:1em;right:1em; } </style> <script>(function () { let tries = 0 function addLogo () { if (typeof slideshow === 'undefined') { tries += 1 if (tries < 10) { setTimeout(addLogo, 100) } } else { document.querySelectorAll('.remark-slide-content:not(.title-slide):not(.inverse):not(.hide_logo)') .forEach(function (slide) { const logo = document.createElement('a') logo.classList = 'xaringan-extra-logo' logo.href = 'https://www.uninove.br' slide.appendChild(logo) }) } } document.addEventListener('DOMContentLoaded', addLogo) })()</script> </div> # O que é modelagem de tópicos? <img src="images/topic-modeling.jpg" width="100%" /> .footnote[ Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415 ] ??? Modelo Probabilístico --- class: inverse, middle, center # Iramuteq vs Topic Modeling <img src="images/fight.jpg" width="200" /> --- .footnote[ Reinert, M. (1990). Alceste une méthodologie d'analyse des données textuelles et une application: Aurelia De Gerard De Nerval. Bulletin of Sociological Methodology/Bulletin de méthodologie sociologique, 26(1), 24-54. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993-1022. ] .pull-left[ ## Iramuteq * Clusterização Hierárquica * Estimativa Pontual * Erro * Muitos "graus de liberdade" do pesquisador * Reinert (1990) - 787 citações ] -- .pull-right[ ## Topic Modeling * Modelo Probabilístico * Densidade Posterior * Incerteza * Apenas um "grau de liberdade" do pesquisador * Blei et al. (2003) - 37.287 citações * Publicado na Nature, PLoS, PNAS etc. * Usado pela Amazon ] ??? Iramuteq é clusterização -> Point Estimate e um único pertencimento Enquanto TM é um modelo generativo probabilístico (bayesiano) -> Densidade posterior completa e uma probabilidade de pertencimento `\(\sum_p =1 e \mathbf{p} = (p_1, \dots, p_k)\)`. --- class: inverse, middle, center # Mas ainda temos que processar texto <img src="images/corpus.jpg" width="75%" /> --- ## Pré-processamento de Texto <div id="htmlwidget-9eceb20dc2a931752ae4" style="width:80%;height:504px;" class="grViz html-widget"></div> <script type="application/json" data-for="htmlwidget-9eceb20dc2a931752ae4">{"x":{"diagram":"\n digraph text_preprocessing {\n graph [overlap = false,\n fontsize = 12,\n rankdir = LR]\n node [shape = oval,\n fontname = Helvetica]\n A [label = \"Pré\nProcessamento\"]\n node [shape = box,\n fontname = Helvetica]\n B [label = \"Tokenização\"]; C [label = \"Lematização\"]; D [label = \"Stop Words\"];\n A -> {B C D } [dir = forward,\n tailport = \"e\",\n headport = \"w\"]\n} \n","config":{"engine":"dot","options":null}},"evals":[],"jsHooks":[]}</script> .footnote[ Denny, M. J., & Spirling, A. (2018). Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It. Political Analysis, 26(2), 168–189. https://doi.org/10.1017/pan.2017.44 Storopoli, J. E. (2019). Topic Modeling: How and why to use in management research. Iberoamerican Journal of Strategic Management (IJSM), 18(3), 8–20. ] --- # Structural Topic Modeling (STM) .small[ * Topic Modeling com Esteroides * Metadados dos documentos para gerar inferências sobre a prevalência e conteúdo de cada tópico * Além de descobrir tópicos * Analisa a relação das informações dos documentos com os tópicos * Farrell (2016) analisou mais de 40 mil documentos sobre mudança climática de 120 organizações * Kuhn (2018) analisou mais de 25 mil relatórios de acidentes de aviação ] .footnote[ Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064–1082. https://doi.org/10.1111/ajps.12103 Farrell, J. (2016). Corporate funding and ideological polarization about climate change. Proceedings of the National Academy of Sciences, 113(1), 92–97. https://doi.org/10.1073/PNAS.1509433112 Kuhn, K. D. (2018). Using structural topic modeling to identify latent topics and trends in aviation incident reports. Transportation Research Part C: Emerging Technologies, 87, 105-122. ] --- class: inverse, middle, center # Ferramentas <svg viewBox="0 0 512 512" style="position:relative;display:inline-block;top:.1em;fill:white;height:6em;" xmlns="http://www.w3.org/2000/svg"> <path d="M501.1 395.7L384 278.6c-23.1-23.1-57.6-27.6-85.4-13.9L192 158.1V96L64 0 0 64l96 128h62.1l106.6 106.6c-13.6 27.8-9.2 62.3 13.9 85.4l117.1 117.1c14.6 14.6 38.2 14.6 52.7 0l52.7-52.7c14.5-14.6 14.5-38.2 0-52.7zM331.7 225c28.3 0 54.9 11 74.9 31l19.4 19.4c15.8-6.9 30.8-16.5 43.8-29.5 37.1-37.1 49.7-89.3 37.9-136.7-2.2-9-13.5-12.1-20.1-5.5l-74.4 74.4-67.9-11.3L334 98.9l74.4-74.4c6.6-6.6 3.4-17.9-5.7-20.2-47.4-11.7-99.6.9-136.6 37.9-28.5 28.5-41.9 66.1-41.2 103.6l82.1 82.1c8.1-1.9 16.5-2.9 24.7-2.9zm-103.9 82l-56.7-56.7L18.7 402.8c-25 25-25 65.5 0 90.5s65.5 25 90.5 0l123.6-123.6c-7.6-19.9-9.9-41.6-5-62.7zM64 472c-13.2 0-24-10.8-24-24 0-13.3 10.7-24 24-24s24 10.7 24 24c0 13.2-10.7 24-24 24z"></path></svg> --- class: middle # R ### [`{stm}`](https://www.structuraltopicmodel.com/) e [`{quanteda}`](http://quanteda.io/) # Python ### [`gensim`](https://radimrehurek.com/gensim/) e [`scikit-learn`](https://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction) # Julia ### [`TextAnalysis.jl`](https://juliahub.com/docs/TextAnalysis) --- class: inverse, middle <img src="images/case-study.jpg" width="100%" /> --- # Senhor dos Anéis - [Kaggle](https://www.kaggle.com/paultimothymooney/lord-of-the-rings-data?select=lotr_scripts.csv) .pull-left[ ### `lotr_scripts.csv` * 2,389 falas * `char`: Personagem * `dialog`: Fala * `movie`: Filme ] .pull-right[ ### `lotr_characters.csv` * 911 personagens * `race`: Elfo, Orc, Humano etc. * `gender`: Male, Female ] .footnote[ Kaggle - https://www.kaggle.com/paultimothymooney/lord-of-the-rings-data ] --- # Preparação dos dados .pull-left[ ```r library(readtext) df <- readtext( "data/lotr_scripts.csv", * text_field = "dialog") ``` ] .pull-right[ * FRODO * SAM * GANDALF * ARAGORN * PIPPIN * MERRY * GOLLUM * GIMLI * LEGOLAS ] --- # Corpus e Tokens ```r library(quanteda) *corpus <- corpus(df) summary(corpus) ``` ``` ## Corpus consisting of 2390 documents, showing 3 documents: ## ## Text Types Tokens Sentences id char movie ## lotr_scripts.csv.1 9 15 1 0 Other The Return of the King ## lotr_scripts.csv.2 9 17 2 1 Other The Return of the King ## lotr_scripts.csv.3 2 2 1 2 Other The Return of the King ``` ```r toks <- tokens(corpus, * remove_punct = TRUE, * remove_symbols = TRUE, * remove_numbers = TRUE, * remove_separators = TRUE, * split_hyphens = TRUE ) ``` --- # Document-Term Matrix (`dtm`) ```r library(stopwords) dfm_mat <- dfm(toks, * tolower = TRUE) dfm_mat <- dfm_remove(dfm_mat, * pattern = stopwords(language = "en", source = "snowball")) dfm_mat <- dfm_wordstem(dfm_mat, * language = "en") dfm_mat ``` ``` ## Document-feature matrix of: 2,390 documents, 8 features (99.45% sparse) and 3 docvars. ## features ## docs smeagol fish pull arrghh deagol love birthday precious ## lotr_scripts.csv.1 3 1 0 0 0 0 0 0 ## lotr_scripts.csv.2 0 0 2 0 0 0 0 0 ## lotr_scripts.csv.3 0 0 0 1 0 0 0 0 ## lotr_scripts.csv.4 0 0 0 0 1 0 0 0 ## lotr_scripts.csv.5 0 0 0 0 1 0 0 0 ## lotr_scripts.csv.6 0 0 0 0 1 0 0 0 ## [ reached max_ndoc ... 2,384 more documents ] ``` --- # Falas x Personagem x Filme <img src="index_files/figure-html/ggplot2-1.png" width="504" style="display: block; margin: auto;" /> --- # Quantos tópicos? .small[ Antes precisamos converter a `dtm` do `{quanteda}` para o `{stm}`: ] ```r dtm_stm <- convert(dfm_mat, to = "stm") ``` <img src="index_files/figure-html/how_many_k-1.png" width="50%" style="display: block; margin: auto;" /> --- # Topic Modeling ```r topic_model <- stm(dtm_stm$documents, vocab = dtm_stm$vocab, data = dtm_stm$meta, * K = 3, * prevalence =~ movie + char, * seed = 123) ``` .large[ ``` ## Topic 1 Top Words: ## Highest Prob: king, lord, gondor, smeagol, rohan, citi ## FREX: king, lord, gondor, smeagol, rohan, citi ## Lift: 3'7, 3'8, aaa, aaaaagh, ab, account ## Score: king, lord, gondor, theoden, smeagol, heh ## Topic 2 Top Words: ## Highest Prob: gandalf, sam, day, death, friend, war ## FREX: gandalf, sam, death, friend, wait, heart ## Lift: aaaaaaaaahhhhhhhhhh, aaaaah, aaaahh, aah, abdollen, accord ## Score: sam, gandalf, hmm, friend, grond, death ## Topic 3 Top Words: ## Highest Prob: frodo, master, hobbit, time, dead, merri ## FREX: frodo, hobbit, time, dead, merri, precious ## Lift: beacon, bilbo, brego, care, carri, eat ## Score: frodo, precious, merri, dead, kill, pippin ``` ] --- <img src="index_files/figure-html/plot-tm-1.png" width="504" style="display: block; margin: auto;" /> --- # STM .large[ ```r regression <- estimateEffect( * 1:3 ~ movie + char, topic_model, meta = dtm_stm$meta) summary(regression, topics = 1) ``` ] --- **Tópico 1**: king, lord, gondor, smeagol, rohan, citi, saruman ``` ## ## Call: ## estimateEffect(formula = 1:3 ~ movie + char, stmobj = topic_model, ## metadata = dtm_stm$meta, uncertainty = "Global") ## ## ## Topic 1: ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.3045 0.0287 10.59 < 2e-16 *** ## movieThe Return of the King 0.1119 0.0184 6.10 1.3e-09 *** ## movieThe Two Towers 0.1042 0.0172 6.06 1.6e-09 *** ## charFRODO -0.1365 0.0334 -4.09 4.5e-05 *** ## charGANDALF 0.0056 0.0318 0.18 0.86026 ## charGIMLI -0.1150 0.0378 -3.04 0.00240 ** ## charGOLLUM -0.1723 0.0351 -4.91 9.6e-07 *** ## charLEGOLAS -0.0327 0.0482 -0.68 0.49684 ## charMERRY -0.0885 0.0385 -2.30 0.02144 * ## charOther -0.0235 0.0252 -0.93 0.35045 ## charPIPPIN -0.1150 0.0335 -3.43 0.00061 *** ## charSAM -0.2510 0.0308 -8.15 6.0e-16 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- **Tópico 2**: gandalf, sam, day, death, friend, war, tree ``` ## ## Call: ## estimateEffect(formula = 1:3 ~ movie + char, stmobj = topic_model, ## metadata = dtm_stm$meta, uncertainty = "Global") ## ## ## Topic 2: ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.31629 0.02627 12.04 < 2e-16 *** ## movieThe Return of the King -0.04632 0.01838 -2.52 0.012 * ## movieThe Two Towers 0.01102 0.01728 0.64 0.524 ## charFRODO 0.13651 0.03166 4.31 1.7e-05 *** ## charGANDALF -0.05346 0.03258 -1.64 0.101 ## charGIMLI 0.02569 0.03912 0.66 0.511 ## charGOLLUM -0.14073 0.03409 -4.13 3.8e-05 *** ## charLEGOLAS -0.02542 0.04409 -0.58 0.564 ## charMERRY -0.00247 0.03799 -0.06 0.948 ## charOther 0.03094 0.02389 1.30 0.195 ## charPIPPIN 0.01262 0.03397 0.37 0.710 ## charSAM 0.02116 0.03067 0.69 0.490 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- **Tópico 3**: frodo, master, hobbit, time, dead, merri, aragorn ``` ## ## Call: ## estimateEffect(formula = 1:3 ~ movie + char, stmobj = topic_model, ## metadata = dtm_stm$meta, uncertainty = "Global") ## ## ## Topic 3: ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 0.379540 0.026614 14.26 < 2e-16 *** ## movieThe Return of the King -0.065841 0.017155 -3.84 0.00013 *** ## movieThe Two Towers -0.115106 0.018120 -6.35 2.6e-10 *** ## charFRODO -0.000351 0.031306 -0.01 0.99105 ## charGANDALF 0.048076 0.032796 1.47 0.14283 ## charGIMLI 0.089524 0.038393 2.33 0.01981 * ## charGOLLUM 0.312678 0.034811 8.98 < 2e-16 *** ## charLEGOLAS 0.057565 0.048128 1.20 0.23180 ## charMERRY 0.091165 0.035883 2.54 0.01114 * ## charOther -0.007891 0.024354 -0.32 0.74595 ## charPIPPIN 0.102262 0.033542 3.05 0.00233 ** ## charSAM 0.229294 0.030934 7.41 1.8e-13 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- class: inverse, middle <img src="images/meme-final.jpg" width="100%" /> --- # Créditos! Slides criado pelo pacote R [`xaringan`](https://github.com/yihui/xaringan). 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