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mise a jour Rmd

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FRANCOIS PELLETIER 9 months ago
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120a075b61
2 changed files with 108 additions and 3 deletions
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      Analyse_BD.Rmd
  2. +5
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      Transformation_BD.Rmd

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Analyse_BD.Rmd View File

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---
title: "Analyse BD"
author: "François Pelletier"
date: "07/10/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library("sentometrics")
library("tidyverse")
library("plotly")
```

```{r}
core_features_corpus.RDS <- readRDS("core_features_corpus.RDS")
top_10_country <- readRDS("top_10_country.RDS")
top_10_sites <- readRDS("top_10_sites.RDS")
corpusSample <- quanteda::corpus_sample(core_features_corpus.RDS, size = 200)
```

# Définition des lexiques

```{r}
data("list_valence_shifters", package = "sentometrics")
data("list_lexicons", package = "sentometrics")

lexIn <- list_lexicons[c("FEEL_en_tr")]
valIn <- list_valence_shifters[["en"]]

l1 <- sento_lexicons(lexIn,valIn)
```

# Calcul des sentiments

```{r}
c_sentiments_sample <- compute_sentiment(x = corpusSample,
lexicons = l1,
how = "counts",
nCore = 8)
c_sentiments_sample
```

```{r}
c_control_compute <- ctr_agg(howWithin = "proportional",
howDocs = "equal_weight",
howTime = "equal_weight",
lag = 7,
by = "day")

c_sentiments <- sento_measures(sento_corpus = core_features_corpus.RDS,
lexicons = l1,
ctr = c_control_compute)
```

```{r}
c_measures <- as.data.table(c_sentiments)
```

```{r}
c_measures_g <- measures_global(c_sentiments)
```

# Sentiment par site

```{r}
c_measures_melt <- c_measures %>%
select(date,starts_with("FEEL_en_tr--site")) %>%
`colnames<-`(c("date",top_10_sites$site)) %>%
melt(id="date",variable.name = "site")
plot_site <- ggplot(data=c_measures_melt,
aes(x=date, y=value, colour=site))+
geom_line()
ggplotly(plot_site)
```

# Sentiment par pays

```{r}
c_measures_melt <- c_measures %>%
select(date,starts_with("FEEL_en_tr--country")) %>%
`colnames<-`(c("date",top_10_country$country)) %>%
melt(id="date",variable.name = "country")
plot_country <- ggplot(data=c_measures_melt,
aes(x=date, y=value, colour=country))+
geom_line()
ggplotly(plot_country)
```

# Sentiment par compteur d'entités

```{r}
c_measures_melt <- c_measures %>%
select(date,starts_with("FEEL_en_tr--entity")) %>%
melt(id="date",variable.name = "entity")
plot_entity <- ggplot(data=c_measures_melt,
aes(x=date, y=value, colour=entity))+
geom_line()
ggplotly(plot_entity)
```

+ 5
- 3
Transformation_BD.Rmd View File

@@ -1,4 +1,4 @@
---
---
title: "Formatage des données"
author: "François Pelletier"
date: "07/10/2019"
@@ -32,6 +32,7 @@ top_10_sites <- tbl(con,"core") %>%
arrange(desc(n)) %>%
head(10) %>%
collect()
saveRDS(top_10_sites,"top_10_sites.RDS")
top_10_sites
```

@@ -44,6 +45,7 @@ top_10_country <- tbl(con,"core") %>%
arrange(desc(n)) %>%
head(10) %>%
collect()
saveRDS(top_10_country,"top_10_country.RDS")
top_10_country
```

@@ -56,7 +58,7 @@ entities_count <- tbl(con,"entities") %>% group_by(uuid,entity_type) %>% count %
```

```{r}
entities_count_t <- entities_count %>% reshape2::dcast(uuid~entity_type,fun.aggregate = sum, value.var = "n")
entities_count_t <- entities_count %>% reshape2::dcast(uuid~paste0("entity_",entity_type),fun.aggregate = sum, value.var = "n")
entities_count_t %>% head(10) %>% glimpse()
```

@@ -73,7 +75,7 @@ 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),
texts=paste(title_full,text,sep = "\n"),
# Site
site_01 = ifelse(site==top_10_sites$site[1],1,0),
site_02 = ifelse(site==top_10_sites$site[2],1,0),


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