Perspective is an API that uses machine learning models to score the perceived impact a comment might have on a conversation. Website.
peRspective provides access to the API using the R programming
language.
For an excellent documentation of the Perspective API see here.
This is a work-in-progress project and I welcome feedback and pull requests!
- Create a Google Cloud project in your Google Cloud console
- Go to Perspective API’s overview page and click Enable
- Go to the API credentials page, just click Create credentials, and choose “API Key”.
Now you are ready to make a request to the Perspective API!
Be sure to check your quota limit! You can learn more about Perspective API quota limit by visiting your google cloud project’s Perspective API page.
The maximum text size per request is 3000 bytes.
For detailed overview of the used models see here.
Here is a list of models currently supported by peRspective:
| Model Attribute Name | Version | Supported Languages | Short Description |
|---|---|---|---|
| TOXICITY | Alpha | en, es, fr*, de* | rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion. |
| SEVERE_TOXICITY | Alpha | en, es, fr*, de* | Same deep-CNN algorithm as TOXICITY, but is trained on ‘very toxic’ labels. |
| IDENTITY_ATTACK | Experimental toxicity sub-attribute | en, fr*, de*, es* | negative or hateful comments targeting someone because of their identity. |
| INSULT | Experimental toxicity sub-attribute | en, fr*, de*, es* | insulting, inflammatory, or negative comment towards a person or a group of people. |
| PROFANITY | Experimental toxicity sub-attribute | en, fr*, de*, es* | swear words, curse words, or other obscene or profane language. |
| SEXUALLY_EXPLICIT | Experimental toxicity sub-attribute | en, fr*, de*, es* | contains references to sexual acts, body parts, or other lewd content. |
| THREAT | Experimental toxicity sub-attribute | en, fr*, de*, es* | describes an intention to inflict pain, injury, or violence against an individual or group. |
| FLIRTATION | Experimental toxicity sub-attribute | en, fr*, de*, es* | pickup lines, complimenting appearance, subtle sexual innuendos, etc. |
| ATTACK_ON_AUTHOR | NYT moderation models | en | Attack on the author of an article or post. |
| ATTACK_ON_COMMENTER | NYT moderation models | en | Attack on fellow commenter. |
| INCOHERENT | NYT moderation models | en | Difficult to understand, nonsensical. |
| INFLAMMATORY | NYT moderation models | en | Intending to provoke or inflame. |
| LIKELY_TO_REJECT | NYT moderation models | en | Overall measure of the likelihood for the comment to be rejected according to the NYT’s moderation. |
| OBSCENE | NYT moderation models | en | Obscene or vulgar language such as cursing. |
| SPAM | NYT moderation models | en | Irrelevant and unsolicited commercial content. |
| UNSUBSTANTIAL | NYT moderation models | en | Trivial or short comments. |
Note: Languages that are annotated with "*" are only accessible in
the _EXPERIMENTAL version of the models. In order to access them just
add to the supplied model string like this: TOXICITY_EXPERIMENTAL.
A character vector that includes all supported models can be obtained like this:
c(
peRspective::prsp_models,
peRspective::prsp_exp_models
)
#> [1] "TOXICITY" "SEVERE_TOXICITY"
#> [3] "IDENTITY_ATTACK" "INSULT"
#> [5] "PROFANITY" "SEXUALLY_EXPLICIT"
#> [7] "THREAT" "FLIRTATION"
#> [9] "ATTACK_ON_AUTHOR" "ATTACK_ON_COMMENTER"
#> [11] "INCOHERENT" "INFLAMMATORY"
#> [13] "LIKELY_TO_REJECT" "OBSCENE"
#> [15] "SPAM" "UNSUBSTANTIAL"
#> [17] "TOXICITY_EXPERIMENTAL" "SEVERE_TOXICITY_EXPERIMENTAL"
#> [19] "IDENTITY_ATTACK_EXPERIMENTAL" "INSULT_EXPERIMENTAL"
#> [21] "PROFANITY_EXPERIMENTAL" "THREAT_EXPERIMENTAL"First, install package from GitHub:
devtools::install_github("favstats/peRspective")Load package:
library(peRspective)Also the tidyverse for examples.
library(tidyverse)Define your key variable.
peRspective functions will read the API key from environment variable
perspective_api_key. In order to add your key to your environment
file, you can use the function edit_r_environ() from the usethis
package.
usethis::edit_r_environ()This will open your .Renviron file in your text editor. Now, you can add the following line to it:
perspective_api_key="YOUR_API_KEY"Save the file and restart R for the changes to take effect.
Alternatively, you can provide an explicit definition of your API key
with each function call using the key argument.
Now you can use prsp_score to score your comments with various models
provided by the Perspective API.
my_text <- "You wrote this? Wow. This is dumb and childish, please go f**** yourself."
text_scores <- prsp_score(
text = my_text,
languages = "en",
score_model = peRspective::prsp_models
)
text_scores %>%
gather() %>%
mutate(key = fct_reorder(key, value)) %>%
ggplot(aes(key, value)) +
geom_col() +
coord_flip() +
ylim(0, 1) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
labs(x = "Model", y = "Probability", title = "Perspective API Results")
A Trump Tweet:
trump_tweet <- "The Fake News Media has NEVER been more Dishonest or Corrupt than it is right now. There has never been a time like this in American History. Very exciting but also, very sad! Fake News is the absolute Enemy of the People and our Country itself!"
text_scores <- prsp_score(
trump_tweet,
score_sentences = F,
score_model = peRspective::prsp_models
)
text_scores %>%
gather() %>%
mutate(key = fct_reorder(key, value)) %>%
ggplot(aes(key, value)) +
geom_col() +
coord_flip() +
ylim(0, 1) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
labs(x = "Model", y = "Probability", title = "Perspective API Results")
Instead of scoring just entire comments you can also score individual
sentences with score_sentences = T. In this case the Perspective API
will automatically split your text into reasonable sentences and score
them in addition to an overall score.
trump_tweet <- "The Fake News Media has NEVER been more Dishonest or Corrupt than it is right now. There has never been a time like this in American History. Very exciting but also, very sad! Fake News is the absolute Enemy of the People and our Country itself!"
text_scores <- prsp_score(
trump_tweet,
score_sentences = T,
score_model = peRspective::prsp_models
)
text_scores %>%
unnest(sentence_scores) %>%
select(type, score, sentences) %>%
gather(value, key, -sentences, -score) %>%
mutate(key = fct_reorder(key, score)) %>%
ggplot(aes(key, score)) +
geom_col() +
coord_flip() +
facet_wrap(~sentences, ncol = 2) +
geom_hline(yintercept = 0.5, linetype = "dashed") +
labs(x = "Model", y = "Probability", title = "Perspective API Results")
You can also use Spanish (es) for TOXICITY, SEVERE_TOXICITY and
_EXPERIMENTAL models.
spanish_text <- "gastan en cosas que de nada sirven-nunca tratan de saber la verdad del funcionalismo de nuestro sistema solar y origen del cosmos-falso por Kepler. LAS UNIVERSIDADES DEL MUNDO NO SABEN ANALIZAR VERDAD O MENTIRA-LO QUE DICE KEPLER"
text_scores <- prsp_score(
text = spanish_text,
languages = "es",
score_model = c("TOXICITY", "SEVERE_TOXICITY", "INSULT_EXPERIMENTAL")
)
text_scores %>%
gather() %>%
mutate(key = fct_reorder(key, value)) %>%
ggplot(aes(key, value)) +
geom_col() +
coord_flip() +
geom_hline(yintercept = 0.5, linetype = "dashed") +
labs(x = "Model", y = "Probability", title = "Perspective API Results")
NOTE: Your provided text will be stored by the Perspective API for
future research. This option is the default. If the supplied texts are
private or any of the authors of the texts are below 13 years old,
doNotStore should be set to TRUE.
So far we have only seen how to get individual comments or sentences
scored. But what if you would like to run the function for an entire
dataset with a text column? This is where prsp_stream comes in. At its
core prsp_stream is a loop implemented within purrr::map to iterate
over your text column. To use it let’s first generate a mock tibble.
text_sample <- tibble(
ctext = c("You wrote this? Wow. This is dumb and childish, please go f**** yourself.",
"I don't know what to say about this but it's not good. The commenter is just an idiot",
"This goes even further!",
"What the hell is going on?",
"Please. I don't get it. Explain it again",
"Annoying and irrelevant! I'd rather watch the paint drying on the wall!"),
textid = c("#efdcxct", "#ehfcsct",
"#ekacxwt", "#ewatxad",
"#ekacswt", "#ewftxwd")
)prsp_stream requires a text and text_id column. It wraps
prsp_score and takes all its arguments. Let’s run the most basic
version:
text_sample %>%
prsp_stream(text = ctext,
text_id = textid,
score_model = c("TOXICITY", "SEVERE_TOXICITY"))
#> Binding rows...
#> # A tibble: 6 x 3
#> text_id TOXICITY SEVERE_TOXICITY
#> <chr> <dbl> <dbl>
#> 1 #efdcxct 0.955 0.794
#> 2 #ehfcsct 0.927 0.436
#> 3 #ekacxwt 0.0562 0.0224
#> 4 #ewatxad 0.666 0.309
#> 5 #ekacswt 0.0694 0.0290
#> 6 #ewftxwd 0.442 0.224You receive a tibble with your desired scorings including the
text_id to match your score with your original dataframe.
Now, the problem is that sometimes the call might fail at some point. It
is therefore suggested to set safe_output = TRUE. This will put the
function into a purrr::safely environment to ensure that your function
will keep running even if you encounter errors.
Let’s try it out with a new dataset that contains text that the Perspective API can’t score
text_sample <- tibble(
ctext = c("You wrote this? Wow. This is dumb and childish, please go f**** yourself.",
"I don't know what to say about this but it's not good. The commenter is just an idiot",
## empty string
"",
"This goes even further!",
"What the hell is going on?",
"Please. I don't get it. Explain it again",
## Gibberish
"kdlfkmgkdfmgkfmg",
"Annoying and irrelevant! I'd rather watch the paint drying on the wall!",
## Gibberish
"Hippi Hoppo"),
textid = c("#efdcxct", "#ehfcsct",
"#ekacxwt", "#ewatxad",
"#ekacswt", "#ewftxwd",
"#eeadswt", "#enfhxed",
"#efdmjd")
)And run the function with safe_output = TRUE.
text_sample %>%
prsp_stream(text = ctext,
text_id = textid,
score_model = c("TOXICITY", "SEVERE_TOXICITY", "INSULT"),
safe_output = T)
#> # A tibble: 9 x 5
#> text_id error TOXICITY SEVERE_TOXICITY INSULT
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 #efdcxct No Error 0.955 0.794 0.923
#> 2 #ehfcsct No Error 0.927 0.436 0.947
#> 3 #ekacxwt "Error in .f(...): HTTP 400\nI… NA NA NA
#> 4 #ewatxad No Error 0.0562 0.0224 0.0315
#> 5 #ekacswt No Error 0.666 0.309 0.331
#> 6 #ewftxwd No Error 0.0694 0.0290 0.0499
#> 7 #eeadswt "Error in .f(...): HTTP 400\nI… NA NA NA
#> 8 #enfhxed No Error 0.442 0.224 0.321
#> 9 #efdmjd "Error in .f(...): HTTP 400\nI… NA NA NAsafe_output = T will also provide us with the error messages that
occured so that we can check what went wrong!
Finally, there is one last argument: verbose = TRUE. Enable this
argument and thanks to crayon you
will receive beautiful console output that guides you along the way,
showing you errors and text scores as you go.
text_sample %>%
prsp_stream(text = ctext,
text_id = textid,
score_model = c("TOXICITY", "SEVERE_TOXICITY"),
verbose = T,
safe_output = T)
Or the (not as pretty) output in Markdown
#> 11.11% [2019-05-22 04:28:30]: 1 out of 9 (11.11%)
#> text_id: #efdcxct
#> 0.96 TOXICITY
#> 0.79 SEVERE_TOXICITY
#>
#> 22.22% [2019-05-22 04:28:31]: 2 out of 9 (22.22%)
#> text_id: #ehfcsct
#> 0.93 TOXICITY
#> 0.44 SEVERE_TOXICITY
#>
#> 33.33% [2019-05-22 04:28:32]: 3 out of 9 (33.33%)
#> text_id: #ekacxwt
#> ERROR
#> Error in .f(...): HTTP 400
#> INVALID_ARGUMENT: Comment must be non-empty.
#> NO SCORES
#>
#> 44.44% [2019-05-22 04:28:33]: 4 out of 9 (44.44%)
#> text_id: #ewatxad
#> 0.06 TOXICITY
#> 0.02 SEVERE_TOXICITY
#>
#> 55.56% [2019-05-22 04:28:34]: 5 out of 9 (55.56%)
#> text_id: #ekacswt
#> 0.67 TOXICITY
#> 0.31 SEVERE_TOXICITY
#>
#> 66.67% [2019-05-22 04:28:35]: 6 out of 9 (66.67%)
#> text_id: #ewftxwd
#> 0.07 TOXICITY
#> 0.03 SEVERE_TOXICITY
#>
#> 77.78% [2019-05-22 04:28:36]: 7 out of 9 (77.78%)
#> text_id: #eeadswt
#> ERROR
#> Error in .f(...): HTTP 400
#> INVALID_ARGUMENT: Attribute SEVERE_TOXICITY does not support request languages: is
#> NO SCORES
#>
#> 88.89% [2019-05-22 04:28:37]: 8 out of 9 (88.89%)
#> text_id: #enfhxed
#> 0.44 TOXICITY
#> 0.22 SEVERE_TOXICITY
#>
#> 100.00% [2019-05-22 04:28:38]: 9 out of 9 (100.00%)
#> text_id: #efdmjd
#> ERROR
#> Error in .f(...): HTTP 400
#> INVALID_ARGUMENT: Attribute SEVERE_TOXICITY does not support request languages: ja-Latn
#> NO SCORES
#> # A tibble: 9 x 4
#> text_id error TOXICITY SEVERE_TOXICITY
#> <chr> <chr> <dbl> <dbl>
#> 1 #efdcxct No Error 0.955 0.794
#> 2 #ehfcsct No Error 0.927 0.436
#> 3 #ekacxwt "Error in .f(...): HTTP 400\nINVALID_A… NA NA
#> 4 #ewatxad No Error 0.0562 0.0224
#> 5 #ekacswt No Error 0.666 0.309
#> 6 #ewftxwd No Error 0.0694 0.0290
#> 7 #eeadswt "Error in .f(...): HTTP 400\nINVALID_A… NA NA
#> 8 #enfhxed No Error 0.442 0.224
#> 9 #efdmjd "Error in .f(...): HTTP 400\nINVALID_A… NA NA
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 17134)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
#> [3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
#> [5] LC_TIME=English_Germany.1252
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] peRspective_0.1.0 forcats_0.4.0 stringr_1.4.0
#> [4] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
#> [7] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
#> [10] tidyverse_1.2.1 badger_0.0.4
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_0.2.5 xfun_0.7 haven_2.1.0
#> [4] lattice_0.20-38 vctrs_0.1.0 colorspace_1.4-1
#> [7] generics_0.0.2 htmltools_0.3.6 yaml_2.2.0
#> [10] utf8_1.1.4 rlang_0.3.4 pillar_1.4.0
#> [13] withr_2.1.2 glue_1.3.1 RColorBrewer_1.1-2
#> [16] modelr_0.1.4 readxl_1.3.1 rvcheck_0.1.3
#> [19] plyr_1.8.4 dlstats_0.1.0 munsell_0.5.0
#> [22] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.4
#> [25] evaluate_0.13 labeling_0.3 knitr_1.23
#> [28] curl_3.3 fansi_0.4.0 highr_0.8
#> [31] broom_0.5.2 Rcpp_1.0.1 scales_1.0.0
#> [34] backports_1.1.4 jsonlite_1.6 hms_0.4.2
#> [37] digest_0.6.19 stringi_1.4.3 rlist_0.4.6.1
#> [40] grid_3.6.0 cli_1.1.0 tools_3.6.0
#> [43] magrittr_1.5 lazyeval_0.2.2 zeallot_0.1.0
#> [46] crayon_1.3.4 pkgconfig_2.0.2 ellipsis_0.1.0
#> [49] data.table_1.12.2 xml2_1.2.0 lubridate_1.7.4
#> [52] assertthat_0.2.1 rmarkdown_1.12.6 httr_1.4.0
#> [55] rstudioapi_0.10 R6_2.4.0 nlme_3.1-139
#> [58] compiler_3.6.0Icons made by Freepik from www.flaticon.com is licensed by CC 3.0 BY
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent