Datasets:

Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 1K<n<10K
Licenses: cc-by-4.0
Language Creators: found
Annotations Creators: expert-generated
Source Datasets: original
Dataset Preview Go to dataset viewer
id (int)verse_text (string)label (class label)
0
with pale blue berries. in these peaceful shades--
1 (positive)
1
it flows so long as falls the rain,
2 (no_impact)
2
and that is why, the lonesome day,
0 (negative)
3
when i peruse the conquered fame of heroes, and the victories of mighty generals, i do not envy the generals,
3 (mixed)
4
of inward strife for truth and liberty.
3 (mixed)
5
the red sword sealed their vows!
3 (mixed)
6
and very venus of a pipe.
2 (no_impact)
7
who the man, who, called a brother.
2 (no_impact)
8
and so on. then a worthless gaud or two,
0 (negative)
9
to hide the orb of truth--and every throne
2 (no_impact)
10
the call's more urgent when he journeys slow.
2 (no_impact)
11
with the _quart d'heure_ of rabelais!
2 (no_impact)
12
and match, and bend, and thorough-blend, in her colossal form and face.
2 (no_impact)
13
have i played in different countries.
2 (no_impact)
14
tells us that the day is ended."
2 (no_impact)
15
and not alone by gold;
2 (no_impact)
16
that has a charmingly bourbon air.
1 (positive)
17
sounded o'er earth and sea its blast of war,
0 (negative)
18
chief poet on the tiber-side
2 (no_impact)
19
as under a sunbeam a cloud ascends,
2 (no_impact)
20
brightly expressive as the twins of leda,
1 (positive)
21
of night, and all things now retir'd to rest
2 (no_impact)
22
in latmian fountains long ago.
2 (no_impact)
23
in monumental pomp! no grecian drop
1 (positive)
24
and when they reached the house,
2 (no_impact)
25
then this old orchard, sloping to the west;
2 (no_impact)
26
so prythee get thee gone.
2 (no_impact)
27
the other dark-eyed dears
2 (no_impact)
28
me honied paths forsake;
2 (no_impact)
29
to that mysterious strand.
2 (no_impact)
30
wid a song up on de way.
2 (no_impact)
31
her visions and those we have seen,--
2 (no_impact)
32
he sat beside the governor and said grace;
2 (no_impact)
33
fifty times the brahmins' offer deluged all the floor.
2 (no_impact)
34
and what are all the prizes won
2 (no_impact)
35
made snow of all the blossoms; at my feet
2 (no_impact)
36
he never told us what he was,
2 (no_impact)
37
want and woe, which torture us,
0 (negative)
38
a ruby, and a pearl, or so,
2 (no_impact)
39
an echo returned on the cold gray morn,
0 (negative)
40
he says he’s hungry,—he would rather have
2 (no_impact)
41
while i, ... i built up follies like a wall
0 (negative)
42
and then he shut his little eyes,
2 (no_impact)
43
ah, what a pang of aching sharp surprise
0 (negative)
44
and gladys said,
2 (no_impact)
45
peep timidly from out its nest,
2 (no_impact)
46
the oriole's fledglings fifty times
2 (no_impact)
47
the hostile cohorts melt away;
3 (mixed)
48
and the old swallow-haunted barns,--
0 (negative)
49
from god's design, with threads of rain!
2 (no_impact)
50
how over, though, for even me who knew
2 (no_impact)
51
warped into adamantine fretwork, hung
2 (no_impact)
52
wilt thou forget the love that joined us here?
2 (no_impact)
53
the which she bearing home it burned her nest,
0 (negative)
54
have roughened in the gales!
2 (no_impact)
55
pilgrim and soldier, saint and sage,
2 (no_impact)
56
down in the west upon the ocean floor
2 (no_impact)
57
"what did you hear, for instance?" willis said.
2 (no_impact)
58
should favour equal to the sons of heaven:
2 (no_impact)
59
some, not so large, in rings,--
2 (no_impact)
60
the crown of sorrow on their heads, their loss
0 (negative)
61
the eternal law,
2 (no_impact)
62
and lips where heavenly smiles would hang and blend
1 (positive)
63
we're a band!" said the weary big dragoon.
2 (no_impact)
64
fu' to ba' de battle's brunt.
2 (no_impact)
65
and brief related whom they brought, wher found,
2 (no_impact)
66
i lay and watched the lonely gloom;
0 (negative)
67
honour to the bugle-horn!
1 (positive)
68
a sceptre,--monstrous, winged, intolerable.
0 (negative)
69
max laid his hand upon the old man's arm,
2 (no_impact)
70
when on the boughs the purple buds expand,
2 (no_impact)
71
if the pure and holy angels
1 (positive)
72
endymion would have passed across the mead
2 (no_impact)
73
upon the thought of perfect noon. and when
1 (positive)
74
thy hands all cunning arts that women prize.
1 (positive)
75
reasoning to admiration, and with mee
1 (positive)
76
while the rude winds blow off each shadowy crown.
0 (negative)
77
the former, as the slacken’d reins he drew
2 (no_impact)
78
she falls back from the freedom she had hoped."
2 (no_impact)
79
then--i would gather it, to thee unaware,
2 (no_impact)
80
amidst the gold and the purple, and the pillows of his bed:
2 (no_impact)
81
all hastening onward, yet none seemed to know
2 (no_impact)
82
the wheat-blade whispers of the sheaf.
2 (no_impact)
83
but o, nevermore can we prison him tight.
0 (negative)
84
under these leafy vaults and walls,
2 (no_impact)
85
(distinctly here the spirit sneezed,)
2 (no_impact)
86
it shines superior on a throne of gold:
1 (positive)
87
around it cling.
2 (no_impact)
88
may meditate a whole youth's loss,
0 (negative)
89
i'm safe enlisted fer the war,
2 (no_impact)
90
whom phoebus taught unerring prophecy,
2 (no_impact)
91
when thee, the eyes of that harsh long ago
0 (negative)
92
flutter,
2 (no_impact)
93
a way that safely will my passage guide.”
2 (no_impact)
94
and breaths were gathering sure
2 (no_impact)
95
you have done this, says one judge; done that, says another;
2 (no_impact)
96
in their archetypes endure.
2 (no_impact)
97
returne, the starres of morn shall see him rise
2 (no_impact)
98
brown-gabled, long, and full of seams
2 (no_impact)
99
the foes inclosing, and his friend pursued,
0 (negative)
End of preview (truncated to 100 rows)

Dataset Card for Gutenberg Poem Dataset

Dataset Summary

Poem Sentiment is a sentiment dataset of poem verses from Project Gutenberg. This dataset can be used for tasks such as sentiment classification or style transfer for poems.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The text in the dataset is in English (en).

Dataset Structure

Data Instances

Example of one instance in the dataset.

{'id': 0, 'label': 2, 'verse_text': 'with pale blue berries. in these peaceful shades--'}

Data Fields

  • id: index of the example
  • verse_text: The text of the poem verse
  • label: The sentiment label. Here
    • 0 = negative
    • 1 = positive
    • 2 = no impact
    • 3 = mixed (both negative and positive)

      Note: The original dataset uses different label indices (negative = -1, no impact = 0, positive = 1)

Data Splits

The dataset is split into a train, validation, and test split with the following sizes:

train validation test
Number of examples 892 105 104

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

This work is licensed under a Creative Commons Attribution 4.0 International License

Citation Information

@misc{sheng2020investigating,
      title={Investigating Societal Biases in a Poetry Composition System},
      author={Emily Sheng and David Uthus},
      year={2020},
      eprint={2011.02686},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Contributions

Thanks to @patil-suraj for adding this dataset.

Update on GitHub
Papers with Code