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Oh my God, he’s lost it. He’s totally lost it.
non-neutral
What?
surprise
Or! Or, we could go to the bank, close our accounts and cut them off at the source.
neutral
You’re a genius!
joy
Aww, man, now we won’t be bank buddies!
sadness
Now, there’s two reasons.
neutral
Hey.
neutral
Hey!
joy
Ohh, you guys, remember that cute client I told you about? I bit him.
neutral
Where?!
surprise
On the touchy.
neutral
And
neutral
No, I know!
surprise
I-I’m sorry, but the moment I touch him, I just wanna throw out my old oath and take a new, dirty one.
non-neutral
Well, next time your massaging him, you should try and distract yourself.
neutral
Yeah! Yeah! Yeah! Like-like when I’m doing something exciting and I don’t wanna get
joy
Thank you, Joey.
neutral
No-no, thank you.
neutral
Hey Estelle, listen
neutral
Well! Well! Well! Joey Tribbiani! So you came back huh? They
surprise
What are you talkin’ about? I never left you! You’ve always been my agent!
surprise
Really?!
surprise
Yeah!
joy
Oh well, no harm, no foul.
neutral
Okay, you guys free tonight?
neutral
Yeah!!
joy
Tonight? You-you didn't say it was going to be at nighttime.
surprise
Yes? Yes?! How can I help you?
neutral
Yeah, we were…we were just looking around.
neutral
Oh-oh, you’re-you’re fellow scholars.
surprise
What exactly were you looking for, hmm?
neutral
Perhaps, perhaps Dr. Chester Stock’s musings on the Smiledon Californicus?
neutral
Uhh….
neutral
Ah… Ah…Get out of here! Uh, meeting someone? Or-or are you just here to brush up on Marion’s views on evolution?
non-neutral
Uh, actually I find Marion’s views far too progressionist.
neutral
I find Marion’s views far too progressionist.
neutral
I’m sorry, who are you?
surprise
I’m a professor here uh, Ross…Geller.
neutral
Ross Geller, why do I know that name? It’s uh—Wait! Did you write this?
surprise
Yes! You’re the person who checked out my book?!
surprise
Y’know, you look nothing like I would’ve thought. You’re…you’re so young.
surprise
Well I uh, I skipped forth grade.
neutral
You had no right to tell me you ever had feelings for me.
anger
What?
surprise
I was doing great with Julie before I found out about you.
anger
Hey, I was doin' great before I found out about you. You think it's easy for me to see you with Julie?
anger
The point is I...
non-neutral
I don't need this right now, OK.
anger
It, it's too late, I'm with somebody else, I'm happy.
non-neutral
This ship has sailed.
non-neutral
Alright, fine, you go ahead and you do that, alright Ross.
non-neutral
Fine.
anger
'Cause I don't need your stupid ship.
anger
Good.
anger
Good.
anger
Oh, it's so romantic to send people off on their honeymoon.
joy
Y’know, Monica and Chandler are married. Ross and Rachel are having a baby. Maybe you and I should do something.
neutral
All in good time my love.
neutral
All in good time.
neutral
Oh shoot!
surprise
I left my guitar in their apartment.
non-neutral
Well you can let me in later.
neutral
I don’t have a key, they took mine to give to you.
neutral
What?! They took mine to give to you!
surprise
Why would they take away our keys?
surprise
Wow! It looks like we got a lot of good stuff.
joy
Oh we did, but my mom got us the greatest gift of all.
joy
A
neutral
No. She’s going to live with us for eight weeks.
neutral
Uh, what?
surprise
Yes! She’s gonna help us take care of the baby! Woo-hoo.
joy
What—You’re not serious.
non-neutral
I mean she’s a very nice woman, but there is no way we can take eight weeks of her.
non-neutral
She’ll drive us totally crazy.
disgust
Hi Ross!
joy
Hi roomie!
joy
Hey! What did you decide to do about the movie?
neutral
I don’t know!
neutral
It’s not like it’s porn!
neutral
This is a serious, legitimate movie.
neutral
Y’know?
neutral
And the nudity is really important to the story.
neutral
That’s what you say about porn.
neutral
You’re right. Maybe I shouldn’t even go on the call back.
neutral
No! No you should! A lot of major actors do nude scenes! I mean, the chance to star in a movie? Come on!
non-neutral
Well that’s true.
neutral
And I am only naked in one scene.
neutral
Plus it sounds really great.
neutral
My character’s catholic and he falls in love with this Jewish girl.
neutral
Who run away together and they get caught in this big rainstorm.
neutral
So we go into this barn and undress each other and hold each other.
neutral
It’s really sweet and-and tender.
joy
Hey, what’s up?
neutral
Nothing, Monica and I had a stupid fight.
sadness
But you’re still moving in together, right? Because my ad came out today.
neutral
"Wanted. Female roommate, non-smoker, non-ugly." Nice!
joy
Yeah?
neutral
I just figured y’know, after living with you it’d be an interesting change of pace to have a female roommate, y’know?
neutral
Someone I can learn from, someone-someone who’s different than me.
neutral
And what’s more different than me; a guy who’s
neutral

Dataset Card for friends_data

Dataset Summary

The Friends dataset consists of speech-based dialogue from the Friends TV sitcom. It is extracted from the SocialNLP EmotionX 2019 challenge.

Supported Tasks and Leaderboards

text-classification, sentiment-classification: The dataset is mainly used to predict a sentiment label given text input.

Languages

The utterances are in English.

Dataset Structure

Data Instances

A data point containing text and the corresponding label.

An example from the friends_dataset looks like this:

{ 'text': 'Well! Well! Well! Joey Tribbiani! So you came back huh?', 'label': 'surprise' }

Data Fields

The field includes a text column and a corresponding emotion label.

Dataset Creation

Curation Rationale

The dataset contains 1000 English-language dialogues originally in JSON files. The JSON file contains an array of dialogue objects. Each dialogue object is an array of line objects, and each line object contains speaker, utterance, emotion, and annotation strings. { "speaker": "Chandler", "utterance": "My duties? All right.", "emotion": "surprise", "annotation": "2000030" }

Utterance and emotion were extracted from the original files into a CSV file. The dataset was cleaned to remove non-neutral labels. This dataset was created to be used in fine-tuning an emotion sentiment classifier that can be useful to teach individuals with autism how to read facial expressions.

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