# Datasets:xed_en_fi

Multilinguality: multilingual
Size Categories: 10K<n<100K 1K<n<10K
Language Creators: found
Annotations Creators: expert-generated
ArXiv:
Dataset Preview
sentence (string)labels (sequence)
", ..."
[ 1 ]
"!"
[ 1, 4, 7 ]
"... And I don't think we need to discuss the Trinity any more ."
[ 8, 1 ]
"* So get up out of your bed"
[ 1 ]
"A confession that you hired [PERSON] ... and are responsible for my father's murder ."
[ 1, 6 ]
"A dead man has one half - hour to raise his roll outside and get back in the game ."
[ 1 ]
"A guy that's talking about he's gonna solve all your problems ."
[ 1 ]
"A guy who was a pain in the neck even before I carried his stuff ."
[ 1 ]
"A hundred of these are produced every day and sent to sweatshops where urban slaves prepare this poison for our friends , our loved ones , our children ."
[ 1, 3 ]
"A lady wouldn't work for this outfit ."
[ 1 ]
"A little dicey doing a background on an FBI agent ."
[ 1 ]
"A little restraint ?"
[ 1 ]
"A little rush ?"
[ 1 ]
"A lost decade , but it was worth it to make your father pay for my loss ."
[ 1 ]
"A lot of junk all right ..."
[ 1 ]
"A lot of people are going to pay for this night ."
[ 1 ]
"A lot of you incumbents will be in danger of losing your seats ."
[ 1 ]
"A man is going to die ."
[ 1, 6 ]
"A map's no good in the outback !"
[ 1 ]
"A perfect place to lose anybody , but this little doll's our cover story ."
[ 1 ]
"A traitor !"
[ 1, 3 ]
"A woman who drinks is bad enough and I will not stand for a woman cursing and blaspheming God ."
[ 1, 3 ]
"[PERSON] , why don't you just ... Lighten up ."
[ 1, 2 ]
[ 1 ]
"Absolute disregard to not show up ."
[ 1 ]
"Acknowledge , over ."
[ 1 ]
"Acts of homicide and cannibalism reported through the afternoon are contributable , at least in part , to these reactivated bodies ."
[ 1, 6 ]
"[PERSON] , I don't think it'd be so bad if I taught him how to throw a few ..."
[ 1 ]
"[PERSON] , what did I do ?"
[ 1 ]
"[PERSON] , where's the kid ?"
[ 1 ]
"After that , I don't care if you've found the Holy Grail ."
[ 1 ]
"After them ! After them !"
[ 1, 2 ]
"After them ... slowly !"
[ 1, 2 ]
"Ah , for fuck's sake , mate ."
[ 1 ]
"Ah ! - You should listen to your brother ."
[ 1, 7 ]
"Ahem . Father brophy was very disturbed ."
[ 1, 6 ]
"Ain't gonna get tied down with no girl ."
[ 1, 2 ]
"Ain't much you can do about us ."
[ 1 ]
"Ain't nobody in the world gonna drive this except me ."
[ 1 ]
"Alas ... Scalpel ."
[ 1, 2 ]
"[PERSON] , will you let me finish ?"
[ 1 ]
"All dead , you mean !"
[ 1 ]
"All hands to rescue stations !"
[ 1, 2, 4 ]
"All happened on your watch ."
[ 1, 3 ]
"All I asked you is where the hell [PERSON] is ."
[ 1 ]
"All I can charge him with is riding on a train . They're gonna walk ."
[ 1 ]
"All I know is that I'd like to meet a member of the judiciary or a member of Congress that thinks that this situation , the status quo is satisfactory ."
[ 1, 3 ]
"All I want to do is make things as difficult as possible ."
[ 1 ]
"All I'm saying is maybe all the child needs is to be loved ."
[ 8, 1, 2 ]
"All other movements are restricted ."
[ 1, 2 ]
"All right , I will !"
[ 1 ]
"All right , next time I hear it , I am taking it away ."
[ 1 ]
"All right , not now , but you need to hear this ."
[ 1 ]
"All right , now , send that woman and her young ' uns out !"
[ 1 ]
"All right , [LOCATION] , that's enough ."
[ 1 ]
"All right , talk !"
[ 1, 2 ]
"All right , what is it if I may ask , You do want to do ?"
[ 1 ]
"All right !"
[ 1, 2, 5 ]
"All right . I'll talk to [PERSON] ."
[ 1 ]
"All right . I've heard enough . I've heard enough ."
[ 1, 3 ]
"All right . Let's go ."
[ 1, 2 ]
"All that Latino macho shit ."
[ 1 ]
"All the officers were disloyal ."
[ 1, 3, 6, 7 ]
"All this talk's just wasting time ."
[ 1 ]
"All those bottles , of sizes different , completely in the wrong order ..."
[ 1 ]
"All those guns and violence ..."
[ 1, 3, 4, 6 ]
"All you have to do ... is point her out and look the other way ."
[ 1, 3, 7 ]
"All : Ohh , shit ."
[ 1, 4 ]
"[PERSON] , what have you done my little sister ?"
[ 1 ]
"Alright , have it any way you want !"
[ 1, 3 ]
"Alright , knock it off !"
[ 1 ]
"Altitude !"
[ 1 ]
"Altitude ? - Altitude . You want some altitude ?"
[ 1 ]
"Am I okay ? Everybody's worried sick about you ."
[ 1 ]
"Am I wrong , Lady ?"
[ 8, 1, 2 ]
"Amateur ."
[ 1, 3 ]
"An executive order ..."
[ 1 ]
"An ... an accident ? Easy , [PERSON] , easy . Watch the rug ."
[ 1, 4, 7 ]
"And actresses are treated like I hate to use the word , but , shit ."
[ 1, 3, 4, 6 ]
"And all that crap ."
[ 1 ]
"And [PERSON] , she won't help me to solve them ."
[ 1 ]
"And don't bother sending a bill , either !"
[ 1 ]
"And don't mess with nobody's girlfriend ."
[ 8, 1 ]
"And don't try anything funny , cos you know what you'll get ."
[ 1 ]
"And don't waste none of his time , because he ain't staying around long ."
[ 1 ]
"And he waded in there with a lead pipe and he saved your ass and now you're going to deny him over his dead body ?"
[ 1 ]
[ 1 ]
"And how do you know that ?"
[ 8, 1 ]
"And how is that different than all the times you risked me ?"
[ 1 ]
"And I ain't gonna repeat what he said ."
[ 1 ]
"And I can't get started ."
[ 1 ]
"And I dangled it in front of him and ripped it away ."
[ 1, 3, 7 ]
"And I don't care whether you go down with him or not ."
[ 1 ]
"And I don't share my husband with anyone !"
[ 1 ]
"And I fear no one , Especially an unholy spirit from an unsavory world ."
[ 1, 2 ]
"And I hate the way you drive , and I hate your stinking whiskey breath ."
[ 1 ]
"And I hate these fucking doughnuts ."
[ 1, 3 ]
"And I stormed out ."
[ 1 ]
"And I want a list of everybody's budgets , research accounts and financials on my desk by tomorrow ."
[ 1 ]
"And I want [PERSON] and [PERSON] ... dead !"
[ 1 ]
End of preview (truncated to 100 rows)

# Dataset Card for xed_english_finnish

### Dataset Summary

This is the XED dataset. The dataset consists of emotion annotated movie subtitles from OPUS. We use Plutchik's 8 core emotions to annotate. The data is multilabel. The original annotations have been sourced for mainly English and Finnish. For the English data we used Stanford NER (named entity recognition) (Finkel et al., 2005) to replace names and locations with the tags: [PERSON] and [LOCATION] respectively. For the Finnish data, we replaced names and locations using the Turku NER corpus (Luoma et al., 2020).

Sentiment Classification, Multilabel Classification, Multilabel Classification, Intent Classification

English, Finnish

## Dataset Structure

### Data Instances

{ "sentence": "A confession that you hired [PERSON] ... and are responsible for my father's murder."
"labels": [1, 6]  # anger, sadness
}


### Data Fields

• sentence: a line from the dataset
• labels: labels corresponding to the emotion as an integer

Where the number indicates the emotion in ascending alphabetical order: anger:1, anticipation:2, disgust:3, fear:4, joy:5, sadness:6, surprise:7, trust:8, with neutral:0 where applicable.

### Data Splits

For English: Number of unique data points: 17528 ('en_annotated' config) + 9675 ('en_neutral' config) Number of emotions: 8 (+neutral)

For Finnish: Number of unique data points: 14449 ('fi_annotated' config) + 10794 ('fi_neutral' config) Number of emotions: 8 (+neutral)