Emotion detection on multiparty dialogue.
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README.md

Emotion Detection

Emotion Detection aims to classify a fine-grained emotion for each utterance in multiparty dialogue. Our annotation is based on the primary emotions in the Feeling Wheel (Willcox, 1982). We must admit that the inter-annotator agreement of this annotation is not the greatest; we welcome any contribution from the community to improve the annotation quality. This task is a part of the Character Mining project led by the Emory NLP research group.

Dataset

Each utterance is annotated with one of the seven emotions, sad, mad, scared, powerful, peaceful, joyful, and neutral, that are the primary emotions in the Feeling Wheel.

Statistics

The following episodes are used for the training, development, and evaluation sets:

  • Train (TRN): [s01_e02, s01_e03, s01_e04, s01_e05, s01_e06, s01_e07, s01_e08, s01_e09, s01_e11, s01_e12, s01_e13, s01_e14, s01_e16, s01_e17, s01_e18, s01_e19, s01_e21, s01_e22, s01_e23, s01_e24, s02_e01, s02_e02, s02_e03, s02_e04, s02_e05, s02_e06, s02_e07, s02_e09, s02_e11, s02_e12, s02_e13, s02_e14, s02_e15, s02_e16, s02_e17, s02_e18, s02_e19, s02_e21, s02_e22, s02_e24, s03_e02, s03_e03, s03_e04, s03_e05, s03_e06, s03_e07, s03_e10, s03_e11, s03_e12, s03_e13, s03_e14, s03_e15, s03_e16, s03_e17, s03_e18, s03_e19, s03_e22, s03_e23, s03_e24, s03_e25, s04_e03, s04_e04, s04_e05, s04_e07, s04_e08, s04_e09, s04_e11, s04_e12, s04_e13, s04_e14, s04_e15, s04_e16, s04_e18, s04_e19, s04_e22, s04_e23, s04_e24]
  • Development (DEV): [s01_e15, s01_e20, s02_e10, s02_e20, s03_e01, s03_e09, s03_e21, s04_e01, s04_e06, s04_e10, s04_e21]
  • Evaluation (TST): [s01_e01, s01_e10, s02_e08, s02_e23, s03_e08, s03_e20, s04_e02, s04_e17, s04_e20]
Dataset Episodes Scenes Utterances
TRN 77 713 9,934
DEV 11 99 1,344
TST 9 85 1,328
Total 97 897 12,606
Dataset Neutral Joyful Peaceful Powerful Scared Mad Sad Total
TRN 3,034 2,184 900 784 1,285 1,076 671 9,934
DEV 393 289 132 134 178 143 75 1,344
TST 349 282 159 145 182 113 98 1,328
Total 3,776 2,755 1,191 1,063 1,645 1,332 844 12,606

Annotation

Each utterance has the field emotion. Three utterances in the following example are annotated with the emotions of Neutral, Joyful, and Powerful, respectively.

{
  "utterance_id": "s01_e02_c01_u002",
  "speakers": ["Joey Tribbiani"],
  "transcript": "Yeah, right!.......Y'serious?",
  "tokens": [
    ["Yeah", ",", "right", "!"],
    ["......."],
    ["Y'serious", "?"]
  ],
  "emotion": "Neutral"
},
{
  "utterance_id": "s01_e02_c01_u003",
  "speakers": ["Phoebe Buffay"],
  "transcript": "Oh, yeah!",
  "tokens": [
    ["Oh", ",", "yeah", "!"]
  ],
  "emotion": "Joyful"
},
{
  "utterance_id": "s01_e02_c01_u004",
  "speakers": ["Rachel Green"],
  "transcript": "Everything you need to know is in that first kiss.",
  "tokens": [
    ["Everything", "you", "need", "to", "know", "is", "in", "that", "first", "kiss", "."]
  ],
  "emotion": "Powerful"
}

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