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Lecturer(s)
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Změlík Richard, doc. Mgr. Ph.D.
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Course content
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The Use of Algorithms in Literary Studies (Linguistics) in the Past and Today. Why Use Computers at All to Solve Problems in Literary Studies? The Possibilities and Limits of Computational Literary Studies: Introductory Lecture Assignment Introduction to the VS Code Environment, Installing Python: Setting Up the Work Environment Jupyter Notebook as a Good Alternative Introduction to variables and the basics of elementary algorithmization Data types: integer, float (first program for solving elementary equations): Simple algebraic problemscreating a calculator to evaluate quadratic or cubic expressions, e.g., (a+b)?, (a+b)? Calculating percentages Pythagorean theorem (a? + b? = c?), calculating the hypotenuse (c = (a? + b?)) based on given values for a and b. Input, Output Data types: string, list (string methods, list methods): Input from an external source Text segmentation, text length, how many times a word (token) occurs in the text Word and lemma frequencies (MorphoDita, working with strings and lists); for loop: Lemmatization in the MorphoDita environment Saving data to a TXT file Lemmat frequency graph Basic text statistics (TTR, text distinctiveness, lexical richness, hapax legomena, entropy); conditions, functions Listing individual values Python libraries (Pandas) 1 Creating a table based on input values (while loop for iterative value insertion): first name, last name, place of residence, address, county, phone number, email, age, ... Saving the table to CSV Loading the table Searching the table by criteria dict data type (dictionary methods, iterating over a dictionary) Loading previous data into a dict from input and upgrading to JSON (first name, ...) Loading JSON and extracting data from it Python Libraries (Pandas and Matplotlib) 2 Loading external TXT files of lemmatized literary texts Listing tokens based on a lemma (working with Pandas) Graphical representation of lemma frequency (working with Matplotlib) Sentiment Analysis 1; case studies: Arbes, Neruda, Mácha (binary model, working with the dict data type, JSON, Pandas, Matplotlib) Graphical model of positive and negative sentiment percentages We will work with the following dataset: sublex_1_0.csv (https://lindat.mff.cuni.cz/repository/items/13991918-3d35-4633-a817-34b383806093) Sentiment Analysis 2; case studies: Mácha (model based on a thematic dictionary) We will work with the JSON format, which contains emotional clusters extracted from the Thematic Thesaurus of the Czech Language (Klégr et al.) We will create a graphical representation of the frequency weights of individual emotional clusters in a given literary text, e.g., love, hate, anxiety, etc. Basic Color Terms in Literary Texts 1 (creating JSON) We will create our own dictionary for BCT Iterating over the dictionary Calculating AF and RF for BCT (iterating over the dictionary) Saving data to an Excel spreadsheet and displaying a bar chart where the color of each bar corresponds to the color of the term Basic Color Terms in Literary Texts 2 (creating custom JSON and charts based on selected literary texts) We will add both AF and RF to the BCT in the dictionary based on color tokens and other associated words, e.g., WHITE = white, whiteness, whitishness, gleaming white, etc. We will create a JSON file in which the titles of works and color categories will be recorded, e.g., {work title 1: {WHITE: 0, }, work title 1: {WHITE: 0, }} We will create a graph showing the trends of color categories across the author's entire body of work. Word Clouds (we are working with a stop-word list) For any literary text, we will create word clouds (a quantitative representation of the main themes) We save the graphs to a folder We automate the loading of specific TXT files and saving them to a folder
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook)
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Learning outcomes
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With the ever-growing influence of digitization and AI on society as a whole, there is a natural need to improve digital literacy and skills. Along with the ongoing development of Digital Humanities, which also touch on literary studies and linguistics (many European universities have long offered programs focused on DH in the context of literary studies and linguistics[1]), there is a growing need for a specialized course that would introduce students to the basics of programming. This elective seminar is designed for that purpose and is aimed at students in literary studies who have no prior experience with programming. The goal of the course is to introduce students to the basics of programming in Python, with a focus on the fundamentals of natural language processing (NLP) and data analysis (CSV and JSON) of literary texts for the purposes of subsequent literary analysis and interpretation. The purpose of the course is not to replace professional programming or to train students of Czech literature to become programmers, but to familiarize them with the possibilities that programming offers for literary studies and linguistics, and to motivate them, if applicable, to work independently in this field and to pursue further self-study. The outcome of the course will be the creation of a very simple script that every student who completes the course will be able to write on their own. This course is not the first of its kind; it builds on previous programming courses offered by the Department of Czech Studies, which received positive feedback.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
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Recommended literature
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BURROWS, F. John. 1992. Computers and the Study of Literature. In: Butler, Christopher S. (ed.): Computers and Written Texts. Oxford, UK - Cambridge, USA: Blackwell, s. 167-204.
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CIULA, Arianna - EIDE, ?yvind. Modelling in Digital Humanities: Signs in Context. Digital Scholarship in the Humanities, roč. 32, č. 1, 2017, s. i33-i46..
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CRAMER, Florian. Post-Digital Literary Studies. MATLIT 4.1, 2016, s. 11-27. (Dostupné z WWW: <http://impactum-journals.uc.pt/matlit/article/view/ 2384/1993).
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CzADH - Ceská asociace pro digitální humanitní vˇedy.
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Český národní korpus.
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EADH - European Association for Digital Humanities.
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freeCodeCamp.org.
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GOODMAN, Nelson. Slova, díla, světy. In: Peregrin, J. (ed. a přel.). Obrat k jazyku: druhé kolo (Jazyk, myšlení a svět v názorech postanalytických filozofů). Praha: Nakladatelství Filozofického ústavu AV CR 1998, s. 129-146..
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HOOVER, David. The microanalysis of style variation. Digital Scholarship in the Humanities, roč. 23, Supplement 2, 2017, s. ii17-ii30..
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JOCKERS, Matthew L. - KIRILLOFF, Gabi. Understanding Gender and Character Agency in the 19th Century Novel. Journal of Cultural Analytics. Dec. 1, 2016..
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Korpus českého verše.
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KŘEN, Michal. Kolokační míry a čeština: srovnání na datech CNK. In: Čermák, F. - Šulc, M. (eds.). Kolokace. Praha: Nakladatelství Lidové noviny 2006, s. 223-248..
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LOTMAN, Jurij M. O exaktnosti v literární vědě. Literární noviny, roč. 15, č. 45, 1966, s. 3..
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Programování v Pythonu.
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Python.
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Python Tutorials for Digital Humanities.
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Python v CR.
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SCHULZ, Kathryn. What is Distant Reading? The New York Times. 26. června 2011. (Dostupné z WWW: <https://www.nytimes.com/2011/06/26/books/review/ the-mechanic-muse-what-is-distant-reading.html).
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w3schools.com.
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Bense, M. Teorie textů. Praha 1967.
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Cohn, Dorrit. (2009). Co dělá fikci fikcí. Praha.
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Cvrček, V. (2013). Kvantitativní analýza kontextu. Praha.
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Čech, R., Popescu, I. I., Altmann, G. (2014). Metody kvantitativní analýzy (nejen) básnických textů. Olomouc.
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ČECH, Radek. Jen popis s čísly? Perspektivy korpusové lingvistiky. Naše řeč, roč. 97, č. 4-5, 2014, s. 171-184..
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Čermák, František. (2010). Lexikon a sémantika. Praha.
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F. Moretti. (2013). Distant Reading. London.
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Jockers, M. L. Macroanalysis: Digital Methods and Literary History. Illionis: University of Illionis 2013..
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JOCKERS, Matthew L. - ARCHEROVÁ, Jodie. (2017). Šifra mistra bestselleru. Anatomie knižního trháku. Přel. J. Podzimek. Praha.
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Levý, J. (1971). Bude literární věda exaktní vědou?. Praha.
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Lotman, Jurij Michajlovič. (1990). Štruktúra umeleckého textu. Bratislava.
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Moretti, F. (2014). Grafy, mapy, stromy: Abstraktní modely literární historie. Praha.
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Moretti, Franco. (1998). Atlas of the European Novel 1800-1900. London.
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MORETTI, Franco.(ed.). (2017). Canon/Archive: Studies in Quantitative Formalism. Brooklyn, New York.
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PETRŮ, Eduard. (1968). Exaktní metody v literárněvědné práci. Olomouc.
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Schmid, Wolf. Narativní transformace, AVČR, Praha 2004.
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Těšitelová, M. (1974). Otázky lexikální statistiky. Praha.
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