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references.bib
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@article{xu_medex_2010,
title = {{MedEx}: a medication information extraction system for clinical narratives},
volume = {17},
issn = {1067-5027, 1527-974X},
shorttitle = {{MedEx}},
url = {https://academic.oup.com/jamia/article-lookup/doi/10.1197/jamia.M3378},
doi = {10.1197/jamia.M3378},
language = {en},
number = {1},
urldate = {2020-06-06},
journal = {Journal of the American Medical Informatics Association},
author = {Xu, H. and Stenner, S. P and Doan, S. and Johnson, K. B and Waitman, L. R and Denny, J. C},
month = jan,
year = {2010},
pages = {19--24},
file = {Texte intégral:C\:\\Users\\33623\\Zotero\\storage\\TIMMA75N\\Xu et al. - 2010 - MedEx a medication information extraction system .pdf:application/pdf},
}
@article{piolat_version_2011,
title = {La version française du dictionnaire pour le {LIWC} : modalités de construction et exemples d’utilisation},
volume = {56},
issn = {00332984},
shorttitle = {La version française du dictionnaire pour le {LIWC}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0033298411000355},
doi = {10.1016/j.psfr.2011.07.002},
language = {fr},
number = {3},
urldate = {2020-07-01},
journal = {Psychologie Française},
author = {Piolat, A. and Booth, R.J. and Chung, C.K. and Davids, M. and Pennebaker, J.W.},
month = sep,
year = {2011},
pages = {145--159},
}
@article{weissman_construct_2019,
title = {Construct validity of six sentiment analysis methods in the text of encounter notes of patients with critical illness},
volume = {89},
issn = {15320464},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1532046418302284},
doi = {10.1016/j.jbi.2018.12.001},
language = {en},
urldate = {2020-07-01},
journal = {Journal of Biomedical Informatics},
author = {Weissman, Gary E. and Ungar, Lyle H. and Harhay, Michael O. and Courtright, Katherine R. and Halpern, Scott D.},
month = jan,
year = {2019},
pages = {114--121},
file = {Texte intégral:C\:\\Users\\33623\\Zotero\\storage\\QBL57PMT\\Weissman et al. - 2019 - Construct validity of six sentiment analysis metho.pdf:application/pdf},
}
@article{muhammad_contextual_2016,
title = {Contextual sentiment analysis for social media genres},
volume = {108},
issn = {09507051},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0950705116301149},
doi = {10.1016/j.knosys.2016.05.032},
language = {en},
urldate = {2020-07-02},
journal = {Knowledge-Based Systems},
author = {Muhammad, Aminu and Wiratunga, Nirmalie and Lothian, Robert},
month = sep,
year = {2016},
pages = {92--101},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\W8RZBLVV\\Muhammad et al. - 2016 - Contextual sentiment analysis for social media gen.pdf:application/pdf},
}
@inproceedings{denecke_using_2008,
address = {Cancun, Mexico},
title = {Using {SentiWordNet} for multilingual sentiment analysis},
isbn = {978-1-4244-2161-9 978-1-4244-2162-6},
url = {http://ieeexplore.ieee.org/document/4498370/},
doi = {10.1109/ICDEW.2008.4498370},
urldate = {2020-07-02},
booktitle = {2008 {IEEE} 24th {International} {Conference} on {Data} {Engineering} {Workshop}},
publisher = {IEEE},
author = {Denecke, Kerstin},
month = apr,
year = {2008},
pages = {507--512},
}
@article{kiritchenko_effect_2017,
title = {The {Effect} of {Negators}, {Modals}, and {Degree} {Adverbs} on {Sentiment} {Composition}},
url = {http://arxiv.org/abs/1712.01794},
abstract = {Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.},
urldate = {2020-07-03},
journal = {arXiv:1712.01794 [cs]},
author = {Kiritchenko, Svetlana and Mohammad, Saif M.},
month = dec,
year = {2017},
note = {arXiv: 1712.01794},
keywords = {Computer Science - Computation and Language},
file = {arXiv Fulltext PDF:C\:\\Users\\33623\\Zotero\\storage\\HUGI9A2T\\Kiritchenko et Mohammad - 2017 - The Effect of Negators, Modals, and Degree Adverbs.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\33623\\Zotero\\storage\\L2UMQSV9\\1712.html:text/html},
}
@book{boullier_opinion_2012,
title = {Opinion mining et sentiment analysis méthodes et outils.},
isbn = {978-2-8218-1887-3 978-2-8218-1227-7 978-2-8218-1226-0},
language = {French.},
author = {Boullier, Dominique and Lohard, Audrey},
year = {2012},
note = {OCLC: 1096948624},
}
@article{liu_sentiment_2012,
title = {Sentiment {Analysis} and {Opinion} {Mining}},
volume = {5},
issn = {1947-4040, 1947-4059},
url = {http://www.morganclaypool.com/doi/abs/10.2200/S00416ED1V01Y201204HLT016},
doi = {10.2200/S00416ED1V01Y201204HLT016},
language = {en},
number = {1},
urldate = {2020-07-03},
journal = {Synthesis Lectures on Human Language Technologies},
author = {Liu, Bing},
month = may,
year = {2012},
pages = {1--167},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\8CK5SSCL\\Liu - 2012 - Sentiment Analysis and Opinion Mining.pdf:application/pdf},
}
@article{mantyla_evolution_2018,
title = {The evolution of sentiment analysis—{A} review of research topics, venues, and top cited papers},
volume = {27},
issn = {15740137},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1574013717300606},
doi = {10.1016/j.cosrev.2017.10.002},
language = {en},
urldate = {2020-07-03},
journal = {Computer Science Review},
author = {Mäntylä, Mika V. and Graziotin, Daniel and Kuutila, Miikka},
month = feb,
year = {2018},
pages = {16--32},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\FZ2H5BMX\\Mäntylä et al. - 2018 - The evolution of sentiment analysis—A review of re.pdf:application/pdf},
}
@inproceedings{baccianella_sentiwordnet_2010,
address = {Valletta, MT},
title = {{SentiWordNet} 3.0: {An} {Enhanced} {Lexical} {Resource} for {Sentiment} {Analysis} and {Opinion} {Mining}.},
volume = {pp. 2200-2204.},
booktitle = {Proceedings of the 7th {Conference} on {Language} {Resources} and {Evaluation}},
author = {Baccianella, Stephano and Esuli, Andrea and Sebastiani, Fabrizio},
year = {2010},
}
@book{elliot_handbook_2008,
address = {New York},
title = {Handbook of approach and avoidance motivation},
isbn = {978-0-8058-6019-1},
publisher = {Psychology Press},
editor = {Elliot, Andrew J.},
year = {2008},
note = {OCLC: ocn214282414},
keywords = {Avoidance (Psychology)},
}
@article{ordenes_unveiling_2017,
title = {Unveiling {What} {Is} {Written} in the {Stars}: {Analyzing} {Explicit}, {Implicit} and {Discourse} {Patterns} of {Sentiment} in {Social} {Media}},
issn = {0093-5301, 1537-5277},
shorttitle = {Unveiling {What} {Is} {Written} in the {Stars}},
url = {https://academic.oup.com/jcr/article-lookup/doi/10.1093/jcr/ucw070},
doi = {10.1093/jcr/ucw070},
language = {en},
urldate = {2020-07-12},
journal = {Journal of Consumer Research},
author = {Ordenes, Francisco Villarroel and Ludwig, Stephan and De Ruyter, Ko and Grewal, Dhruv and Wetzels, Martin},
month = jan,
year = {2017},
pages = {ucw070},
file = {Version acceptée:C\:\\Users\\33623\\Zotero\\storage\\DLHJ8I9U\\Ordenes et al. - 2017 - Unveiling What Is Written in the Stars Analyzing .pdf:application/pdf},
}
@article{chan_sentiment_2017,
title = {Sentiment analysis in financial texts},
volume = {94},
issn = {01679236},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0167923616301828},
doi = {10.1016/j.dss.2016.10.006},
language = {en},
urldate = {2020-07-12},
journal = {Decision Support Systems},
author = {Chan, Samuel W.K. and Chong, Mickey W.C.},
month = feb,
year = {2017},
pages = {53--64},
}
@article{grand_valley_state_university_interaction_2018,
title = {The {Interaction} {Between} {Microblog} {Sentiment} and {Stock} {Returns}: {An} {Empirical} {Examination}},
volume = {42},
issn = {02767783, 21629730},
shorttitle = {The {Interaction} {Between} {Microblog} {Sentiment} and {Stock} {Returns}},
url = {https://misq.org/the-interaction-between-microblog-sentiment-and-stock-returns-an-empirical-examination.html?SID=a8c3ub0214v89bip4a0qktvs05},
doi = {10.25300/MISQ/2018/14268},
number = {3},
urldate = {2020-07-12},
journal = {MIS Quarterly},
author = {{Grand Valley State University} and Deng, Shuyuan and Huang, Zhijian (James) and {Rochester Institute of Technology} and Sinha, Atish P. and {University of Wisconsin - Milwaukee} and Zhao, Huimin and {University of Wisconsin - Milwaukee}},
month = mar,
year = {2018},
pages = {895--918},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\7DKWR23D\\Grand Valley State University et al. - 2018 - The Interaction Between Microblog Sentiment and St.pdf:application/pdf},
}
@article{taboada_sentiment_2016,
title = {Sentiment {Analysis}: {An} {Overview} from {Linguistics}},
volume = {2},
issn = {2333-9683, 2333-9691},
shorttitle = {Sentiment {Analysis}},
url = {http://www.annualreviews.org/doi/10.1146/annurev-linguistics-011415-040518},
doi = {10.1146/annurev-linguistics-011415-040518},
language = {en},
number = {1},
urldate = {2020-07-22},
journal = {Annual Review of Linguistics},
author = {Taboada, Maite},
month = jan,
year = {2016},
pages = {325--347},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\8Q8SYSRU\\Taboada - 2016 - Sentiment Analysis An Overview from Linguistics.pdf:application/pdf},
}
@incollection{park_good_2011,
address = {Berlin, Heidelberg},
title = {Good {Friends}, {Bad} {News} - {Affect} and {Virality} in {Twitter}},
volume = {185},
isbn = {978-3-642-22308-2 978-3-642-22309-9},
url = {http://link.springer.com/10.1007/978-3-642-22309-9_5},
urldate = {2020-07-24},
booktitle = {Future {Information} {Technology}},
publisher = {Springer Berlin Heidelberg},
author = {Hansen, Lars Kai and Arvidsson, Adam and Nielsen, Finn Aarup and Colleoni, Elanor and Etter, Michael},
editor = {Park, James J. and Yang, Laurence T. and Lee, Changhoon},
year = {2011},
doi = {10.1007/978-3-642-22309-9_5},
note = {Series Title: Communications in Computer and Information Science},
pages = {34--43},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\4HK4XNZB\\Hansen et al. - 2011 - Good Friends, Bad News - Affect and Virality in Tw.pdf:application/pdf},
}
@article{arnold_tidy_2017,
title = {A {Tidy} {Data} {Model} for {Natural} {Language} {Processing} using {cleanNLP}},
volume = {9},
issn = {2073-4859},
url = {https://journal.r-project.org/archive/2017/RJ-2017-035/index.html},
doi = {10.32614/RJ-2017-035},
abstract = {Recent advances in natural language processing have produced libraries that extract lowlevel features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. Specific annotations provided include tokenization, part of speech tagging, named entity recognition, sentiment analysis, dependency parsing, coreference resolution, and word embeddings. The package currently supports input text in English, German, French, and Spanish.},
language = {en},
number = {2},
urldate = {2019-08-11},
journal = {The R Journal},
author = {Arnold, Taylor},
year = {2017},
pages = {248},
}
@article{van_der_maaten_laurens_visualizing_2008,
title = {Visualizing {Data} using t-{SNE}},
journal = {Journal of Machine learning},
author = {{Van der Maaten, Laurens} and Hinton, Geoffrey},
year = {2008},
pages = {2579--2605},
}
@article{shirdastian_using_2019,
title = {Using big data analytics to study brand authenticity sentiments: {The} case of {Starbucks} on {Twitter}},
volume = {48},
issn = {02684012},
shorttitle = {Using big data analytics to study brand authenticity sentiments},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0268401217302657},
doi = {10.1016/j.ijinfomgt.2017.09.007},
language = {en},
urldate = {2019-10-08},
journal = {International Journal of Information Management},
author = {Shirdastian, Hamid and Laroche, Michel and Richard, Marie-Odile},
month = oct,
year = {2019},
pages = {291--307},
}
@article{gunter_sentiment_2014,
title = {Sentiment {Analysis}: {A} {Market}-{Relevant} and {Reliable} {Measure} of {Public} {Feeling}?},
volume = {56},
issn = {1470-7853, 2515-2173},
shorttitle = {Sentiment {Analysis}},
url = {http://journals.sagepub.com/doi/10.2501/IJMR-2014-014},
doi = {10.2501/IJMR-2014-014},
abstract = {This paper critically examines emergent research with sentiment analysis tools to assess their current status and relevance to applied opinion and behaviour measurement. The rapid spread of online news and online chatter in blogs, micro-blogs and social media sites has created a potentially rich source of public opinion. Waves of public feeling are vented spontaneously on a wide range of issues on a minute-by-minute basis in the online world. These online discourses are continually being refreshed, and businesses and advertisers, governments and policy makers have woken up to the fact that this universe of self-perpetuating human sentiment could represent a valuable resource to guide political and business decisions. The massive size of this repository of emotional content renders manual analysis of it feasible only for tiny portions of its totality, and even then can be labour intensive. Computer scientists have however produced software tools that can apply linguistic rules to provide electronic readings of meanings and emotions. These tools are now being utilised by applied social science and market researchers to yield sentiment profiles from online discourses created within specific platforms that purport to represent reliable substitutes for more traditional, offline measures of public opinion. This paper considers what these tools have demonstrated so far and where caution in their application is still called for.},
language = {en},
number = {2},
urldate = {2019-10-20},
journal = {International Journal of Market Research},
author = {Gunter, Barrie and Koteyko, Nelya and Atanasova, Dimitrinka},
month = mar,
year = {2014},
pages = {231--247},
}
@article{tausczik_psychological_2010,
title = {The {Psychological} {Meaning} of {Words}: {LIWC} and {Computerized} {Text} {Analysis} {Methods}},
volume = {29},
issn = {0261-927X, 1552-6526},
shorttitle = {The {Psychological} {Meaning} of {Words}},
url = {http://journals.sagepub.com/doi/10.1177/0261927X09351676},
doi = {10.1177/0261927X09351676},
language = {en},
number = {1},
urldate = {2019-11-07},
journal = {Journal of Language and Social Psychology},
author = {Tausczik, Yla R. and Pennebaker, James W.},
month = mar,
year = {2010},
pages = {24--54},
}
@inproceedings{nielsen_new_2011,
series = {{CEUR} {Workshop} {Proceedings}},
title = {A {New} {ANEW}: {Evaluation} of a {Word} {List} for {Sentiment} {Analysis} in {Microblogs}.},
volume = {718},
url = {http://dblp.uni-trier.de/db/conf/msm/msm2011.html#Nielsen11},
booktitle = {\#{MSM}},
publisher = {CEUR-WS.org},
author = {Nielsen, Finn Årup},
editor = {Rowe, Matthew and Stankovic, Milan and Dadzie, Aba-Sah and Hardey, Mariann},
year = {2011},
keywords = {dblp},
pages = {93--98},
}
@inproceedings{ding_holistic_2008,
address = {New York, NY, USA},
series = {{WSDM} '08},
title = {A {Holistic} {Lexicon}-based {Approach} to {Opinion} {Mining}},
isbn = {978-1-59593-927-2},
url = {http://doi.acm.org/10.1145/1341531.1341561},
doi = {10.1145/1341531.1341561},
booktitle = {Proceedings of the 2008 {International} {Conference} on {Web} {Search} and {Data} {Mining}},
publisher = {ACM},
author = {Ding, Xiaowen and Liu, Bing and Yu, Philip S.},
year = {2008},
keywords = {context dependent opinions, opinion mining, sentiment analysis},
pages = {231--240},
}
@article{duval_analyse_2016,
title = {L'analyse automatisée du ton médiatique : construction et utilisation de la version française du \textit{{Lexicoder}} {Sentiment} {Dictionary}},
volume = {49},
issn = {0008-4239, 1744-9324},
shorttitle = {L'analyse automatisée du ton médiatique},
url = {https://www.cambridge.org/core/product/identifier/S000842391600055X/type/journal_article},
doi = {10.1017/S000842391600055X},
abstract = {Résumé Cet article introduit un nouveau dictionnaire permettant l'analyse automatisée du ton des médias francophones, que nous avons appelé Lexicoder Sentiment Dictionnaire Français ( LSDFr ) en référence au lexique anglophone de Young et Soroka (2012), Lexicoder Sentiment Dictionary ( LSD ) à partir duquel le LSDFr a été construit. Une fois construit, nous comparons le LSDFr au seul autre dictionnaire francophone existant de ce genre, Linguistic Inquiry and Word Count ( LIWC ). Nous testons ensuite la validité interne du LSDFr en le comparant avec un corpus de textes codés manuellement. Nous testons enfin la validité externe du LSDFr en mesurant jusqu'où le ton médiatique, calculé à l'aide de notre dictionnaire, prédit les intentions de vote des Québécois lors des quatre dernières campagnes électorales. En développant cet outil, notre objectif est de permettre à d'autres chercheurs d'effectuer des analyses médiatiques dans un corpus de textes comparables en français. , Abstract This article introduces a new dictionary for the automated analysis of the tone of French media. We named it the French Lexicoder Sentiment Dictionary ( LSDFr ) in reference to the English lexicon developed by Young and Soroka (2012), the Lexicoder Sentiment Dictionary ( LSD ), from which the LSDFr was built. We compare the LSDFr to the only other French sentiment lexicon, Linguistic Inquiry and Word Count ( LIWC ). First, we detail the construction of the dictionary. We then test the internal validity of the LSDFr comparing it with a corpus of manually coded texts. Finally, we test the external validity of LSDFr by measuring how the media tone, calculated using our dictionary, predicts voting intentions in the last four Quebec elections. Our goal is to enable other researchers to conduct media analyses with a comparable corpus of texts in French.},
language = {en},
number = {2},
urldate = {2020-04-10},
journal = {Canadian Journal of Political Science},
author = {Duval, Dominic and Pétry, François},
month = jun,
year = {2016},
pages = {197--220},
}
@book{banda_large-scale_2020,
title = {A large-scale {COVID}-19 {Twitter} chatter dataset for open scientific research - an international collaboration},
copyright = {Open Access},
url = {https://zenodo.org/record/3757272},
abstract = {{\textbackslash}textlessstrong{\textbackslash}textgreaterDue to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage.{\textbackslash}textless/strong{\textbackslash}textgreater {\textbackslash}textlessstrong{\textbackslash}textgreaterThe data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full\_dataset.tsv file (205,409,413 unique tweets), and a cleaned version with no retweets on the full\_dataset-clean.tsv file (44,726,568{\textbackslash}textless/strong{\textbackslash}textgreater{\textbackslash}textlessstrong{\textbackslash}textgreater unique tweets). There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms in frequent\_terms.csv, the top 1000 bigrams in frequent\_bigrams.csv, and the top 1000 trigrams in frequent\_trigrams.csv. Some general statistics per day are included for both datasets in the statistics-full\_dataset.tsv and statistics-full\_dataset-clean.tsv files. {\textbackslash}textless/strong{\textbackslash}textgreater {\textbackslash}textlessstrong{\textbackslash}textgreaterMore details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19\_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) {\textbackslash}textless/strong{\textbackslash}textgreater {\textbackslash}textlessstrong{\textbackslash}textgreaterAs always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. The need to be hydrated to be used. {\textbackslash}textless/strong{\textbackslash}textgreater},
language = {en},
urldate = {2020-04-25},
publisher = {Zenodo},
author = {Banda, Juan M. and Tekumalla, Ramya and Wang, Guanyu and Yu, Jingyuan and Liu, Tuo and Ding, Yuning and Chowell, Gerardo},
month = apr,
year = {2020},
doi = {10.5281/ZENODO.3757272},
keywords = {covid-19, covid19, nlp, social media, twitter},
}
@article{pearce_no_2003,
title = {[{No} title found]},
volume = {28},
issn = {0921030X},
url = {http://link.springer.com/10.1023/A:1022917721797},
doi = {10.1023/A:1022917721797},
number = {2/3},
urldate = {2020-08-22},
journal = {Natural Hazards},
author = {Pearce, Laurie},
year = {2003},
pages = {211--228},
}
@article{roberts_structural_2014,
title = {Structural {Topic} {Models} for {Open}-{Ended} {Survey} {Responses}: {STRUCTURAL} {TOPIC} {MODELS} {FOR} {SURVEY} {RESPONSES}},
volume = {58},
issn = {00925853},
shorttitle = {Structural {Topic} {Models} for {Open}-{Ended} {Survey} {Responses}},
url = {http://doi.wiley.com/10.1111/ajps.12103},
doi = {10.1111/ajps.12103},
language = {en},
number = {4},
urldate = {2020-08-23},
journal = {American Journal of Political Science},
author = {Roberts, Margaret E. and Stewart, Brandon M. and Tingley, Dustin and Lucas, Christopher and Leder-Luis, Jetson and Gadarian, Shana Kushner and Albertson, Bethany and Rand, David G.},
month = oct,
year = {2014},
pages = {1064--1082},
file = {Version acceptée:C\:\\Users\\33623\\Zotero\\storage\\KXCQEYLT\\Roberts et al. - 2014 - Structural Topic Models for Open-Ended Survey Resp.pdf:application/pdf},
}
@article{balech_first_2020,
title = {The {First} {French} {COVID19} {Lockdown} {Twitter} {Dataset}},
url = {http://arxiv.org/abs/2005.05075},
abstract = {In this paper, we present a mainly French coronavirus Twitter dataset that we have been continuously collecting since lockdown restrictions have been enacted in France (in March 17, 2020). We offer our datasets and sentiment analysis annotations to the research community at https://github.com/calciu/COVID19-LockdownFr. They have been obtained using high performance computing (HPC) capabilities of our university's datacenter. We think that our contribution can facilitate analysis of online conversation dynamics reflecting people sentiments when facing severe home confinement restrictions determined by the outbreak of this world wide epidemic. We hope that our contribution will help decode shared experience and mood but also test the sensitivity of sentiment measurement instruments and incite the development of new instruments, methods and approaches.},
urldate = {2020-08-23},
journal = {arXiv:2005.05075 [cs]},
author = {Balech, Sophie and Benavent, Christophe and Calciu, Mihai},
month = may,
year = {2020},
note = {arXiv: 2005.05075},
keywords = {Computer Science - Social and Information Networks, J.4},
file = {arXiv Fulltext PDF:C\:\\Users\\33623\\Zotero\\storage\\Y3RMPSWX\\Balech et al. - 2020 - The First French COVID19 Lockdown Twitter Dataset.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\33623\\Zotero\\storage\\PDEU5L9Q\\2005.html:text/html},
}
@article{cambria_jumping_2014,
title = {Jumping {NLP} {Curves}: {A} {Review} of {Natural} {Language} {Processing} {Research} [{Review} {Article}]},
volume = {9},
issn = {1556-603X},
shorttitle = {Jumping {NLP} {Curves}},
url = {http://ieeexplore.ieee.org/document/6786458/},
doi = {10.1109/MCI.2014.2307227},
number = {2},
urldate = {2020-08-23},
journal = {IEEE Computational Intelligence Magazine},
author = {Cambria, Erik and White, Bebo},
month = may,
year = {2014},
pages = {48--57},
}
@article{anastasopoulos_computational_2017,
title = {Computational {Text} {Analysis} for {Public} {Management} {Research}},
issn = {1556-5068},
url = {https://www.ssrn.com/abstract=3269520},
doi = {10.2139/ssrn.3269520},
language = {en},
urldate = {2020-08-23},
journal = {SSRN Electronic Journal},
author = {Anastasopoulos, Lefteris Jason and Moldogaziev, Tima T. and Scott, Tyler},
year = {2017},
}
@article{kobayashi_text_2018,
title = {Text {Mining} in {Organizational} {Research}},
volume = {21},
issn = {1094-4281, 1552-7425},
url = {http://journals.sagepub.com/doi/10.1177/1094428117722619},
doi = {10.1177/1094428117722619},
language = {en},
number = {3},
urldate = {2020-08-23},
journal = {Organizational Research Methods},
author = {Kobayashi, Vladimer B. and Mol, Stefan T. and Berkers, Hannah A. and Kismihók, Gábor and Den Hartog, Deanne N.},
month = jul,
year = {2018},
pages = {733--765},
file = {Texte intégral:C\:\\Users\\33623\\Zotero\\storage\\4XKYYB2Z\\Kobayashi et al. - 2018 - Text Mining in Organizational Research.pdf:application/pdf},
}
@article{kozlowski_geometry_2019,
title = {The {Geometry} of {Culture}: {Analyzing} the {Meanings} of {Class} through {Word} {Embeddings}},
volume = {84},
issn = {0003-1224, 1939-8271},
shorttitle = {The {Geometry} of {Culture}},
url = {http://journals.sagepub.com/doi/10.1177/0003122419877135},
doi = {10.1177/0003122419877135},
abstract = {We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture. Dimensions induced by word differences ( rich – poor) in these spaces correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared associations, which we validate with surveys. Analyzing text from millions of books published over 100 years, we show that the markers of class continuously shifted amidst the economic transformations of the twentieth century, yet the basic cultural dimensions of class remained remarkably stable. The notable exception is education, which became tightly linked to affluence independent of its association with cultivated taste.},
language = {en},
number = {5},
urldate = {2020-08-23},
journal = {American Sociological Review},
author = {Kozlowski, Austin C. and Taddy, Matt and Evans, James A.},
month = oct,
year = {2019},
pages = {905--949},
file = {Version soumise:C\:\\Users\\33623\\Zotero\\storage\\5CLHUEUR\\Kozlowski et al. - 2019 - The Geometry of Culture Analyzing the Meanings of.pdf:application/pdf},
}
@article{bourdieu_opinion_1973,
title = {L'opinion publique n'existe pas},
number = {n°318},
journal = {Les Temps modernes},
author = {Bourdieu, Pierre},
month = jan,
year = {1973},
pages = {1292--1309},
}
@article{lock_quantitative_2015,
title = {Quantitative content analysis as a method for business ethics research},
volume = {24},
issn = {09628770},
url = {http://doi.wiley.com/10.1111/beer.12095},
doi = {10.1111/beer.12095},
language = {en},
urldate = {2020-11-15},
journal = {Business Ethics: A European Review},
author = {Lock, Irina and Seele, Peter},
month = jul,
year = {2015},
pages = {S24--S40},
}
@article{benoit_quanteda_2018,
title = {quanteda: {An} {R} package for the quantitative analysis of textual data},
volume = {3},
issn = {2475-9066},
shorttitle = {quanteda},
url = {http://joss.theoj.org/papers/10.21105/joss.00774},
doi = {10.21105/joss.00774},
number = {30},
urldate = {2019-01-15},
journal = {Journal of Open Source Software},
author = {Benoit, Kenneth and Watanabe, Kohei and Wang, Haiyan and Nulty, Paul and Obeng, Adam and Müller, Stefan and Matsuo, Akitaka},
month = oct,
year = {2018},
pages = {774},
}
@article{coleman_computer_1975,
title = {A computer readability formula designed for machine scoring.},
volume = {60},
issn = {0021-9010},
url = {http://content.apa.org/journals/apl/60/2/283},
doi = {10.1037/h0076540},
language = {en},
number = {2},
urldate = {2019-01-16},
journal = {Journal of Applied Psychology},
author = {Coleman, Meri and Liau, T. L.},
year = {1975},
pages = {283--284},
}
@inproceedings{canini_online_2009,
address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA},
series = {Proceedings of {Machine} {Learning} {Research}},
title = {Online {Inference} of {Topics} with {Latent} {Dirichlet} {Allocation}},
volume = {5},
url = {http://proceedings.mlr.press/v5/canini09a.html},
abstract = {Inference algorithms for topic models are typically designed to be run over an entire collection of documents after they have been observed. However, in many applications of these models, the collection grows over time, making it infeasible to run batch algorithms repeatedly. This problem can be addressed by using online algorithms, which update estimates of the topics as each document is observed. We introduce two related Rao-Blackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model – incremental Gibbs samplers and particle filters – and compare their runtime and performance to that of existing algorithms.},
booktitle = {Proceedings of the {Twelth} {International} {Conference} on {Artificial} {Intelligence} and {Statistics}},
publisher = {PMLR},
author = {Canini, Kevin and Shi, Lei and Griffiths, Thomas},
editor = {Dyk, David van and Welling, Max},
month = apr,
year = {2009},
pages = {65--72},
}
@article{suster_investigation_2015,
title = {An investigation into language complexity of {World}-of-{Warcraft} game-external texts},
url = {http://arxiv.org/abs/1502.02655},
abstract = {We present a language complexity analysis of World of Warcraft (WoW) community texts, which we compare to texts from a general corpus of web English. Results from several complexity types are presented, including lexical diversity, density, readability and syntactic complexity. The language of WoW texts is found to be comparable to the general corpus on some complexity measures, yet more specialized on other measures. Our findings can be used by educators willing to include game-related activities into school curricula.},
urldate = {2019-01-21},
journal = {arXiv:1502.02655 [cs]},
author = {Šuster, Simon},
month = feb,
year = {2015},
keywords = {Computer Science - Computation and Language},
}
@article{sievert_ldavis_2014,
title = {{LDAvis}: {A} method for visualizing and interpreting topics},
volume = {Baltimore, Maryland, USA},
journal = {Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces.},
author = {Sievert, Carson},
month = jun,
year = {2014},
pages = {63--70},
}
@book{sneath_numerical_1973,
address = {San Francisco},
series = {A {Series} of books in biology},
title = {Numerical taxonomy: the principles and practice of numerical classification},
isbn = {978-0-7167-0697-7},
shorttitle = {Numerical taxonomy},
publisher = {W. H. Freeman},
author = {Sneath, P. H. A. and Sokal, Robert R.},
year = {1973},
keywords = {Numerical taxonomy},
}
@article{liu_towards_2017,
title = {Towards better analysis of machine learning models: {A} visual analytics perspective},
volume = {1},
issn = {2468502X},
shorttitle = {Towards better analysis of machine learning models},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2468502X17300086},
doi = {10.1016/j.visinf.2017.01.006},
language = {en},
number = {1},
urldate = {2018-12-22},
journal = {Visual Informatics},
author = {Liu, Shixia and Wang, Xiting and Liu, Mengchen and Zhu, Jun},
month = mar,
year = {2017},
pages = {48--56},
}
@article{ribeiro_why_2016,
title = {"{Why} {Should} {I} {Trust} {You}?": {Explaining} the {Predictions} of {Any} {Classifier}},
shorttitle = {"{Why} {Should} {I} {Trust} {You}?},
url = {http://arxiv.org/abs/1602.04938},
abstract = {Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one. In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.},
urldate = {2018-12-22},
journal = {arXiv:1602.04938 [cs, stat]},
author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
month = feb,
year = {2016},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}
@article{tibshirani_regression_2011,
title = {Regression shrinkage and selection via the lasso: a retrospective: {Regression} {Shrinkage} and {Selection} via the {Lasso}},
volume = {73},
issn = {13697412},
shorttitle = {Regression shrinkage and selection via the lasso},
url = {http://doi.wiley.com/10.1111/j.1467-9868.2011.00771.x},
doi = {10.1111/j.1467-9868.2011.00771.x},
language = {en},
number = {3},
urldate = {2018-12-22},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
author = {Tibshirani, Robert},
month = jun,
year = {2011},
pages = {273--282},
}
@article{goldstein_peeking_2015,
title = {Peeking {Inside} the {Black} {Box}: {Visualizing} {Statistical} {Learning} {With} {Plots} of {Individual} {Conditional} {Expectation}},
volume = {24},
issn = {1061-8600, 1537-2715},
shorttitle = {Peeking {Inside} the {Black} {Box}},
url = {http://www.tandfonline.com/doi/full/10.1080/10618600.2014.907095},
doi = {10.1080/10618600.2014.907095},
language = {en},
number = {1},
urldate = {2018-12-22},
journal = {Journal of Computational and Graphical Statistics},
author = {Goldstein, Alex and Kapelner, Adam and Bleich, Justin and Pitkin, Emil},
month = jan,
year = {2015},
pages = {44--65},
}
@article{selya_practical_2012,
title = {A {Practical} {Guide} to {Calculating} {Cohen}’s f2, a {Measure} of {Local} {Effect} {Size}, from {PROC} {MIXED}},
volume = {3},
issn = {1664-1078},
url = {http://journal.frontiersin.org/article/10.3389/fpsyg.2012.00111/abstract},
doi = {10.3389/fpsyg.2012.00111},
urldate = {2018-12-24},
journal = {Frontiers in Psychology},
author = {Selya, Arielle S. and Rose, Jennifer S. and Dierker, Lisa C. and Hedeker, Donald and Mermelstein, Robin J.},
year = {2012},
}
@article{pekar_discovery_2008,
title = {Discovery of subjective evaluations of product features in hotel reviews},
volume = {14},
issn = {1356-7667, 1479-1870},
url = {http://journals.sagepub.com/doi/10.1177/1356766707087522},
doi = {10.1177/1356766707087522},
language = {en},
number = {2},
urldate = {2019-01-15},
journal = {Journal of Vacation Marketing},
author = {Pekar, Viktor and {Shiyan Ou}},
month = apr,
year = {2008},
pages = {145--155},
}
@article{puschmann_turning_2018,
title = {Turning {Words} {Into} {Consumer} {Preferences}: {How} {Sentiment} {Analysis} {Is} {Framed} in {Research} and the {News} {Media}},
volume = {4},
issn = {2056-3051, 2056-3051},
shorttitle = {Turning {Words} {Into} {Consumer} {Preferences}},
url = {http://journals.sagepub.com/doi/10.1177/2056305118797724},
doi = {10.1177/2056305118797724},
language = {en},
number = {3},
urldate = {2019-01-15},
journal = {Social Media + Society},
author = {Puschmann, Cornelius and Powell, Alison},
month = jul,
year = {2018},
pages = {205630511879772},
}
@article{hug_loi_2004,
title = {La loi de {Menzerath} appliquée à un ensemble de textes},
journal = {Lexicometrica},
author = {Hug, Marc},
year = {2004},
}
@article{senter_automated_1967,
title = {Automated {Readability} {Index}},
author = {Senter, R.J.},
month = nov,
year = {1967},
}
@article{plutchik_psychoevolutionary_1982,
title = {A psychoevolutionary theory of emotions},
volume = {21},
issn = {0539-0184, 1461-7412},
url = {http://journals.sagepub.com/doi/10.1177/053901882021004003},
doi = {10.1177/053901882021004003},
language = {en},
number = {4-5},
urldate = {2019-01-18},
journal = {Social Science Information},
author = {Plutchik, Robert},
month = jul,
year = {1982},
pages = {529--553},
}
@article{tweedie_how_1998,
title = {How {Variable} {May} a {Constant} be? {Measures} of {Lexical} {Richness} in {Perspective}},
volume = {32},
journal = {Computers and the Humanities},
author = {Tweedie, Fiona J. and Baayen, R. Harald},
year = {1998},
pages = {323--352},
}
@article{sigmund_panel_2017,
title = {Panel {Vector} {Autoregression} in {R} with the {Package} {Panelvar}},
issn = {1556-5068},
url = {https://www.ssrn.com/abstract=2896087},
doi = {10.2139/ssrn.2896087},
language = {en},
urldate = {2019-01-27},
journal = {SSRN Electronic Journal},
author = {Sigmund, Michael and Ferstl, Robert},
year = {2017},
}
@article{gijsenberg_losses_2015,
title = {Losses {Loom} {Longer} than {Gains}: {Modeling} the {Impact} of {Service} {Crises} on {Perceived} {Service} {Quality} over {Time}},
volume = {52},
issn = {0022-2437, 1547-7193},
shorttitle = {Losses {Loom} \textit{{Longer}} than {Gains}},
url = {http://journals.sagepub.com/doi/10.1509/jmr.14.0140},
doi = {10.1509/jmr.14.0140},
language = {en},
number = {5},
urldate = {2019-01-27},
journal = {Journal of Marketing Research},
author = {Gijsenberg, Maarten J. and Van Heerde, Harald J. and Verhoef, Peter C.},
month = oct,
year = {2015},
pages = {642--656},
}
@techreport{bennani_les_2019,
type = {{EconomiX} {Working} {Papers}},
title = {Les déterminants locaux de la participation numérique au {Grand} débat national: une analyse économétrique},
url = {https://EconPapers.repec.org/RePEc:drm:wpaper:2019-7},
abstract = {This paper analyses the local determinants of the electronic participation to the "Grand débat". First, we highlight the spatial heterogeneity of the participants using their zip code. Second, we use an econometric approach to assess the local determinants of the general participation and the participation on each of the four topics of the "Grand débat". The results show that the median standard of living and the education level are the main determinants of the general participation, whereas some specific variables explain the participation of each of the four topics.},
number = {2019-7},
institution = {University of Paris Nanterre, EconomiX},
author = {Bennani, Hamza and Gandré, Pauline and Monnery, Benjamin},
year = {2019},
keywords = {electronic participation, Grand débat, local determinants},
}
@book{armstrong-warwick_data_nodate,
title = {Data in {Your} {Language}: {The} {ECI} {Multilingual} {Corpus} 1},
author = {Armstrong-warwick, Susan and Thompson, Henry S. and McKelvie, David and Petitpierre, Dominique},
}
@book{isabelle_serca_les_2010,
address = {Paris},
series = {« {Recherches} proustiennes »},
title = {{LES} {COUTURES} {APPARENTES} {DE} {LA} {RECHERCHE} {PROUST} {ET} {LA} {PONCTUATION}},
publisher = {Honoré Champion},
author = {{ISABELLE SERCA}},
year = {2010},
}
@article{humphreys_automated_2018,
title = {Automated {Text} {Analysis} for {Consumer} {Research}},
volume = {44},
issn = {0093-5301, 1537-5277},
url = {https://academic.oup.com/jcr/article/44/6/1274/4283031},
doi = {10.1093/jcr/ucx104},
language = {en},
number = {6},
urldate = {2019-07-13},
journal = {Journal of Consumer Research},
author = {Humphreys, Ashlee and Wang, Rebecca Jen-Hui},
editor = {Fischer, Eileen and Price, Linda},
month = apr,
year = {2018},
pages = {1274--1306},
}
@article{blei_latent_2003,
title = {Latent {Dirichlet} {Allocation}},
volume = {3},
issn = {1532-4435},
url = {http://dl.acm.org/citation.cfm?id=944919.944937},
journal = {J. Mach. Learn. Res.},
author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
month = mar,
year = {2003},
pages = {993--1022},
}
@article{canut_sociolinguistique_2000,
title = {De la sociolinguistique à la sociologie du langage : de l'usage des frontières},
volume = {91},
issn = {0181-4095, 2101-0382},
shorttitle = {De la sociolinguistique à la sociologie du langage},
url = {http://www.cairn.info/revue-langage-et-societe-2000-1-page-89.htm},
doi = {10.3917/ls.091.0089},
language = {fr},
number = {1},
urldate = {2019-07-14},
journal = {Langage et société},
author = {Canut, Cécile},
year = {2000},
pages = {89},
}
@article{abdaoui_feel_2017,
title = {{FEEL}: a {French} {Expanded} {Emotion} {Lexicon}},
volume = {51},
issn = {1574-020X, 1574-0218},
shorttitle = {{FEEL}},
url = {http://link.springer.com/10.1007/s10579-016-9364-5},
doi = {10.1007/s10579-016-9364-5},
language = {en},
number = {3},
urldate = {2019-07-14},
journal = {Language Resources and Evaluation},
author = {Abdaoui, Amine and Azé, Jérôme and Bringay, Sandra and Poncelet, Pascal},
month = sep,
year = {2017},
pages = {833--855},
}
@article{mohammad_crowdsourcing_2013,
title = {{CROWDSOURCING} {A} {WORD}-{EMOTION} {ASSOCIATION} {LEXICON}},
volume = {29},
issn = {08247935},
url = {http://doi.wiley.com/10.1111/j.1467-8640.2012.00460.x},
doi = {10.1111/j.1467-8640.2012.00460.x},
language = {en},
number = {3},
urldate = {2019-07-14},
journal = {Computational Intelligence},
author = {Mohammad, Saif M. and Turney, Peter D.},
month = aug,
year = {2013},
pages = {436--465},
}
@article{fruchterman_graph_1991,
title = {Graph drawing by force-directed placement},
volume = {21},
issn = {00380644, 1097024X},
url = {http://doi.wiley.com/10.1002/spe.4380211102},
doi = {10.1002/spe.4380211102},
language = {en},
number = {11},
urldate = {2019-08-11},
journal = {Software: Practice and Experience},
author = {Fruchterman, Thomas M. J. and Reingold, Edward M.},
month = nov,
year = {1991},
pages = {1129--1164},
}
@article{arnold_tidy_2017-1,
title = {A {Tidy} {Data} {Model} for {Natural} {Language} {Processing} using {cleanNLP}},
volume = {9},
issn = {2073-4859},
url = {https://journal.r-project.org/archive/2017/RJ-2017-035/index.html},
doi = {10.32614/RJ-2017-035},
abstract = {Recent advances in natural language processing have produced libraries that extract lowlevel features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. Specific annotations provided include tokenization, part of speech tagging, named entity recognition, sentiment analysis, dependency parsing, coreference resolution, and word embeddings. The package currently supports input text in English, German, French, and Spanish.},
language = {en},
number = {2},
urldate = {2019-08-11},
journal = {The R Journal},
author = {Arnold, Taylor},
year = {2017},
pages = {248},
}
@article{hornik_textcat_2013,
title = {The \textbf{textcat} {Package} for n -{Gram} {Based} {Text} {Categorization} in \textit{{R}}},
volume = {52},
issn = {1548-7660},
url = {http://www.jstatsoft.org/v52/i06/},
doi = {10.18637/jss.v052.i06},
language = {en},
number = {6},
urldate = {2019-08-11},
journal = {Journal of Statistical Software},
author = {Hornik, Kurt and Mair, Patrick and Rauch, Johannes and Geiger, Wilhelm and Buchta, Christian and Feinerer, Ingo},
year = {2013},
}
@article{firth_synopsis_1957,
title = {A synopsis of linguistic theory 1930-55.},
volume = {1952-59},
abstract = {Reprinted in: Palmer, F. R. (ed.) (1968). Selected Papers of J. R. Firth 1952-59, pages 168-205. Longmans, London.},
journal = {Studies in Linguistic Analysis (special volume of the Philological Society)},
author = {Firth, J. R.},
year = {1957},
keywords = {classic linguistics meanign relatedness semantic},
pages = {1--32},
}
@book{chomsky_aspects_1969,
series = {The {MIT} {Press}},
title = {Aspects of the {Theory} of {Syntax}},
isbn = {978-0-262-26050-3},
url = {https://books.google.fr/books?id=u0ksbFqagU8C},
publisher = {MIT Press},
author = {Chomsky, N.},
year = {1969},
}
@article{grishman_message_1997,
title = {Message {Understanding} {Conference}- 6: {A} {Brief} {History}},
abstract = {We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations.},
language = {en},
author = {Grishman, Ralph and Sundheim, Beth},
year = {1997},
pages = {6},
}
@article{wickham_layered_2010,
title = {A {Layered} {Grammar} of {Graphics}},
volume = {19},
issn = {1061-8600, 1537-2715},
url = {http://www.tandfonline.com/doi/abs/10.1198/jcgs.2009.07098},
doi = {10.1198/jcgs.2009.07098},
language = {en},
number = {1},
urldate = {2019-08-15},
journal = {Journal of Computational and Graphical Statistics},
author = {Wickham, Hadley},
month = jan,
year = {2010},
pages = {3--28},
}
@book{r_core_team_r_2020,
address = {Vienna, Austria},
title = {R: {A} {Language} and {Environment} for {Statistical} {Computing}},
url = {https://www.R-project.org/},
publisher = {R Foundation for Statistical Computing},
author = {{R Core Team}},
year = {2020},
}
@book{xie_bookdown_2021,
title = {bookdown: {Authoring} {Books} and {Technical} {Documents} with {R} {Markdown}},
author = {Xie, Yihui},
year = {2021},
}
@book{xie_knitr_2021,
title = {knitr: {A} {General}-{Purpose} {Package} for {Dynamic} {Report} {Generation} in {R}},
url = {https://yihui.org/knitr/},
author = {Xie, Yihui},
year = {2021},
}
@book{allaire_rmarkdown_2021,
title = {rmarkdown: {Dynamic} {Documents} for {R}},
url = {https://CRAN.R-project.org/package=rmarkdown},
author = {Allaire, J. J. and Xie, Yihui and McPherson, Jonathan and Luraschi, Javier and Ushey, Kevin and Atkins, Aron and Wickham, Hadley and Cheng, Joe and Chang, Winston and Iannone, Richard},
year = {2021},
}
@incollection{stodden_knitr_2014,
title = {knitr: {A} {Comprehensive} {Tool} for {Reproducible} {Research} in {R}},