Create automated build

public/2023-08-delta/search.json

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     "title": "12  Working with Text Data in R",
-    "section": "12.4 Tidy Text to Non-Tidy Text Workflows",
-    "text": "12.4 Tidy Text to Non-Tidy Text Workflows\n\n\n\nA flowchart of a typical text analysis that combines tidytext with other tools and data formats, particularly the tm or quanteda packages. Source: Silge & Robinson\n\n\nMany text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. A common approach is to create a document term matrix (DTM), also called a document-feature matrix (DFM). In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns.\nIn the Tidy Text Workflow Exercise, we converted our corpus into a data table that has “one-token-per-row”. However, the tidy text format of “one-token-per-row” is not a common format for other R packages that work with text data or perform text analysis. Packages like tm, quanteda, topicmodels\n\n\n\n\n\n\nR Text Mining Tools and Analysis Packages Resources\n\n\n\n\nCRAN Task View: Natural Language Processing\nPenn Libraries Guides: Text Analysis\n\n\n\n\n\n\n\n\n\nFarrell, Maxwell J., Liam Brierley, Anna Willoughby, Andrew Yates, and Nicole Mideo. 2022. “Past and Future Uses of Text Mining in Ecology and Evolution.” Proceedings of the Royal Society B: Biological Sciences 289 (1975). https://doi.org/10.1098/rspb.2021.2721.\n\n\nFroehlich, Halley E., Rebecca R. Gentry, Michael B. Rust, Dietmar Grimm, and Benjamin S. Halpern. 2017. “Public Perceptions of Aquaculture: Evaluating Spatiotemporal Patterns of Sentiment Around the World.” Edited by Christopher M. Somers. PLOS ONE 12 (1): e0169281. https://doi.org/10.1371/journal.pone.0169281.\n\n\nVan Houtan, Kyle S., Tyler Gagne, Clinton N. Jenkins, and Lucas Joppa. 2020. “Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions.” Patterns 1 (1): 100005. https://doi.org/10.1016/j.patter.2020.100005."
+    "section": "12.4 Tidy Text to Non-tidy Text Workflows",
+    "text": "12.4 Tidy Text to Non-tidy Text Workflows\n\n\n\nA flowchart of a typical text analysis that combines tidytext with other tools and data formats, particularly the tm or quanteda packages. Source: Silge & Robinson\n\n\nIn the Tidy Text Workflow Exercise, we converted our corpus into a data table that has “one-token-per-row”. However, the tidy text format of one-token-per-row is not a common format for other R packages that work with text data or perform text analysis. Packages like tm, quanteda, topicmodels.\nMany text analysis methods, in particular NLP techniques (e.g. topic models) require text data to be stored in a mathematical format. A common approach is to create a matrix, such as a: sparse matrix, a document term matrix (DTM), or a document-feature matrix (DFM). In a matrix format, algorithms are able to more easily compare one document to many other documents to identify patterns.\n\n12.4.1 What is a Document Term Matrix (DTM)?\nA DTM or document-feature matrix (DFM)\nSilge and Robinson kept this in mind as they built the tidytext package, and included helpful cast() functions to turn a tidy text object (again a table with one-token-per-row) into a matrix.\n\n\n12.4.2 Use cast() to Convert to a Matrix (Non-tidy) Format\nlet’s create a matrix of all books we looked at\n\n# download corpus\nall_books_corp <- gutenberg_download(c(175, # phantom of the opera\n                                       42, # jekyll & hyde\n                                       84, # frankenstein\n                                       345), # dracula\n                                     meta_fields = c(\"title\"))\n\n\n# turn corpus into tidy text format\ntidy_all_books <- all_books_corp %>% \n    unnest_tokens(output = word, # output col created \n                  input = text # input col that is split\n                  ) %>% \n    count(title, word)\n\n\n# convert tidy text table to spare matrix from `Matrix` package\n# requires `Matrix` to be installed\nall_books_sparse <- tidy_all_books %>% \n    cast_sparse(row = title,\n                column = word,\n                value = n)\n\n\n# convert tidy text table to DTM object from `tm` package\n# requires `tm` to be installed\nall_books_dtm <- tidy_all_books %>% \n    cast_dtm(term = word,\n             document = title,\n             value = n)\n\n\n# convert tidy text table to DFM object from `quanteda` package\n# requires `quanteda` to be installed\nall_books_dfm <- tidy_all_books %>% \n    cast_dfm(term = word, \n             document = title, \n             value = n)\n\n\n\n\n\n\n\nR Text Mining Tools and Analysis Packages Resources\n\n\n\n\nCRAN Task View: Natural Language Processing\nPenn Libraries Guides: Text Analysis\n\n\n\n\n\n\n\n\n\nFarrell, Maxwell J., Liam Brierley, Anna Willoughby, Andrew Yates, and Nicole Mideo. 2022. “Past and Future Uses of Text Mining in Ecology and Evolution.” Proceedings of the Royal Society B: Biological Sciences 289 (1975). https://doi.org/10.1098/rspb.2021.2721.\n\n\nFroehlich, Halley E., Rebecca R. Gentry, Michael B. Rust, Dietmar Grimm, and Benjamin S. Halpern. 2017. “Public Perceptions of Aquaculture: Evaluating Spatiotemporal Patterns of Sentiment Around the World.” Edited by Christopher M. Somers. PLOS ONE 12 (1): e0169281. https://doi.org/10.1371/journal.pone.0169281.\n\n\nVan Houtan, Kyle S., Tyler Gagne, Clinton N. Jenkins, and Lucas Joppa. 2020. “Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions.” Patterns 1 (1): 100005. https://doi.org/10.1016/j.patter.2020.100005."
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public/2023-08-delta/session_03.html

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 <span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>        <span class="at">popup =</span> <span class="sc">~</span> restore_loc</span>
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 <span id="cb34-17"><a href="#cb34-17" aria-hidden="true" tabindex="-1"></a>        <span class="at">color =</span> <span class="st">"white"</span>,</span>
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