From 104d649bb987ffba8fdf021498731ec2a7a8a521 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Fran=C3=A7ois?= Date: Mon, 7 Oct 2019 17:50:48 -0400 Subject: [PATCH] =?UTF-8?q?transformation=20des=20donn=C3=A9es=20et=20pr?= =?UTF-8?q?=C3=A9sentation?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Preparation_BD.Rmd | 13 ++-- Presentation.Rmd | 79 +++++++++++++++++++ Transformation_BD.Rmd | 108 ++++++++++++++++++++++++++ images/sentometrics_functionality.png | Bin 0 -> 113999 bytes 4 files changed, 194 insertions(+), 6 deletions(-) create mode 100644 Presentation.Rmd create mode 100644 Transformation_BD.Rmd create mode 100644 images/sentometrics_functionality.png diff --git a/Preparation_BD.Rmd b/Preparation_BD.Rmd index c66ac0c..b33e609 100644 --- a/Preparation_BD.Rmd +++ b/Preparation_BD.Rmd @@ -1,5 +1,5 @@ --- -title: "Presentation Sentometrics" +title: "Préparation des données Sentometrics" author: "François Pelletier" date: "06/10/2019" output: html_document @@ -124,7 +124,6 @@ file_news <- list.files(path = "google_news_blogs/news",pattern = "*.json",full. ``` ```{r} - traiter_json <- function(file_path){ json_contents <- jsonlite::read_json(file_path) @@ -133,7 +132,12 @@ traiter_json <- function(file_path){ dbAppendTable(con,"core",core_df) dbAppendTable(con,"entities",entities_df) } +``` + +# Traitement des fichiers + +```{r eval=FALSE} i <- 0 # itérateur for (file_blog in file_blogs){ if(!(i %% 1000)){ @@ -143,10 +147,6 @@ for (file_blog in file_blogs){ i <- i+1 } -``` - - -```{r} ii <- 0 # itérateur for (file_article in file_news){ if(!(ii %% 1000)){ @@ -155,5 +155,6 @@ for (file_article in file_news){ traiter_json(file_article) ii <- ii+1 } + ``` diff --git a/Presentation.Rmd b/Presentation.Rmd new file mode 100644 index 0000000..c9d3ffb --- /dev/null +++ b/Presentation.Rmd @@ -0,0 +1,79 @@ +--- +title: "Sentometrics - Présentation CAAMD" +author: "François Pelletier" +date: "07/10/2019" +output: + beamer_presentation: default + slidy_presentation: default +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = FALSE) +``` + +## Sentometrics + +Présentation basée sur un atelier présenté par Keven Bluteau à **R à Québec 2019**. + +D'où vient le nom ? + +- Mélange d'analyse de sentiments et d'économétrie +- Type d'analyse de plus en plus fréquent en finance, en marketing et en politique. + +Quelle forme prend le produit ? + +- Package R +- Services conseils ($) + +## Article de référence + +[The R Package sentometrics to Compute, Aggregate and Predict with Textual Sentiment](https://ssrn.com/abstract=3067734) + +## Pourquoi ? + +Les données qualitatives sont de plus en plus utilisées pour raffiner les analyses prédictives, car elle donnent une rétroaction sur la passé et le futur, contrairement aux données numériques qui donnent toujours une image passée ou présente d'une réalité. + +## Historique des packages R + +- [tm](https://cran.r-project.org/web/packages/tm/index.html) (2008) +- [openNLP](https://cran.r-project.org/web/packages/openNLP/index.html) (2016) +- [quanteda](https://cran.r-project.org/web/packages/quanteda/index.html) (2018) +- [tidytext](https://cran.r-project.org/web/packages/tidytext/index.html) (2016) + +## Les bases + +Sentometrics est construit sur `quanteda` et `data.table`. Les modèles sont estimés avec `glmnet` et `caret`. + +## Les fonctionnalités + +![](images/sentometrics_functionality.png) + +## Calcul des sentiments + + +- Unigrammes: somme pondérée des scores pour tous les mots apparaissant dans un lexique +- Bigrammes avec décalage de polarité (valence shifting) + - Va intégrer l'impact de mots négatifs par exemple (good vs. not good) +- Groupements avec décalage de polarité (valence shifting) + - Fenêtre mobile avant et après le mot + +## Aggrégation des sentiments + +Les sentiments sont aggrégés en deux phases: + +- Pour tous les documents durant une période donnée +- Pour plusieurs périodes consécutives + +## Création des métriques + +- Aggrégation à l'intérieur du document (howWithin) +- Aggrégation à l'intérieur d'un intervalle de temps (howDocs) +- Aggrégation au fil du temps (howTime) + +## Modélisation + +- Régression avec Elastic Net +- Configuration des hyperparamètres avec `ctr_model()` +- Entraînement avec `sento_model()` + + diff --git a/Transformation_BD.Rmd b/Transformation_BD.Rmd new file mode 100644 index 0000000..de7e321 --- /dev/null +++ b/Transformation_BD.Rmd @@ -0,0 +1,108 @@ +--- +title: "Formatage des données" +author: "François Pelletier" +date: "07/10/2019" +output: html_document +--- + +```{r} +library("tidyverse") +library("RSQLite") +library("DBI") +library("sentometrics") +``` + + +```{r} +con = dbConnect(drv = RSQLite::SQLite(), dbname="google_news.sqlite") +``` + +# Aperçu + +```{r} +tbl(con,"core") %>% head(10) %>% collect() %>% glimpse() +``` + +# Top 10 de modalités +```{r} +top_10_sites <- tbl(con,"core") %>% + select(site) %>% + group_by(site) %>% + count() %>% + arrange(desc(n)) %>% + head(10) %>% + collect() +top_10_sites +``` + +```{r} +top_10_country <- tbl(con,"core") %>% + select(country) %>% + mutate(country = ifelse(country=="","XX",country)) %>% + group_by(country) %>% + count() %>% + arrange(desc(n)) %>% + head(10) %>% + collect() +top_10_country +``` + +# Entities features + +## Compteurs + +```{r} +entities_count <- tbl(con,"entities") %>% group_by(uuid,entity_type) %>% count %>% collect() +``` + +```{r} +entities_count_t <- entities_count %>% reshape2::dcast(uuid~entity_type,fun.aggregate = sum, value.var = "n") +``` + +## Aperçu + +```{r} +tbl(con,"entities") %>% group_by(entity_type, entity) %>% count() %>% arrange(desc(n)) %>% head(100) %>% collect() +``` + +# Core features + +```{r} +core_features_corpus <- tbl(con,"core") %>% collect() %>% + transmute( + id=uuid, + date=lubridate::as_datetime(published), + texts=paste(title_full,text,sep = "\n") %>% substr(start = 1,stop = 1000), + # Site + site_01 = ifelse(site==top_10_sites$site[1],1,0), + site_02 = ifelse(site==top_10_sites$site[2],1,0), + site_03 = ifelse(site==top_10_sites$site[3],1,0), + site_04 = ifelse(site==top_10_sites$site[4],1,0), + site_05 = ifelse(site==top_10_sites$site[5],1,0), + site_06 = ifelse(site==top_10_sites$site[6],1,0), + site_07 = ifelse(site==top_10_sites$site[7],1,0), + site_08 = ifelse(site==top_10_sites$site[8],1,0), + site_09 = ifelse(site==top_10_sites$site[9],1,0), + site_10 = ifelse(site==top_10_sites$site[10],1,0), + # Site type + is_blog = ifelse(site_type=="blogs",1,0), + # Country + country_01 = ifelse(country==top_10_country$country[1],1,0), + country_02 = ifelse(country==top_10_country$country[2],1,0), + country_03 = ifelse(country==top_10_country$country[3],1,0), + country_04 = ifelse(country==top_10_country$country[4],1,0), + country_05 = ifelse(country==top_10_country$country[5],1,0), + country_06 = ifelse(country==top_10_country$country[6],1,0), + country_07 = ifelse(country==top_10_country$country[7],1,0), + country_08 = ifelse(country==top_10_country$country[8],1,0), + country_09 = ifelse(country==top_10_country$country[9],1,0), + country_10 = ifelse(country==top_10_country$country[10],1,0) + ) %>% left_join(entities_count_t,by=c("id"="uuid")) %>% sento_corpus() +``` + +```{r} +saveRDS(core_features_corpus,file = 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