Dataset Viewer
scene_idx
int64 1
14
| valence
float64 0.05
0.95
| arousal
float64 0.3
0.95
| dominance
float64 0.1
0.9
| label
stringlengths 3
14
|
|---|---|---|---|---|
1
| 0.75
| 0.6
| 0.9
|
pride
|
2
| 0.2
| 0.925
| 0.5
|
anger
|
3
| 0.43
| 0.83
| 0.41
|
astonishment
|
4
| 0.388
| 0.75
| 0.312
|
nervousness
|
5
| 0.175
| 0.783
| 0.333
|
tension
|
6
| 0.2
| 0.9
| 0.3
|
fear
|
7
| 0.63
| 0.61
| 0.46
|
longing
|
8
| 0.3
| 0.6
| 0.3
|
worry
|
9
| 0.5
| 0.5
| 0.5
|
longing
|
1
| 0.3
| 0.6
| 0.3
|
worry
|
2
| 0.2
| 0.95
| 0.4
|
anger
|
3
| 0.4
| 0.5
| 0.3
|
confusion
|
4
| 0.425
| 0.9
| 0.525
|
astonishment
|
5
| 0.75
| 0.675
| 0.55
|
fascination
|
6
| 0.5
| 0.5
| 0.5
|
longing
|
7
| 0.9
| 0.5
| 0.8
|
compassion
|
8
| 0.9
| 0.5
| 0.733
|
grateful
|
1
| 0.5
| 0.5
| 0.5
|
longing
|
2
| 0.7
| 0.6
| 0.6
|
interest
|
3
| 0.2
| 0.8
| 0.4
|
tension
|
4
| 0.7
| 0.6
| 0.6
|
interest
|
5
| 0.45
| 0.875
| 0.35
|
astonishment
|
1
| 0.8
| 0.5
| 0.6
|
hope
|
2
| 0.225
| 0.825
| 0.45
|
tension
|
3
| 0.2
| 0.95
| 0.2
|
shock
|
4
| 0.77
| 0.58
| 0.5
|
sympathy
|
5
| 0.5
| 0.5
| 0.5
|
longing
|
6
| 0.5
| 0.5
| 0.5
|
longing
|
7
| 0.183
| 0.5
| 0.333
|
disappointment
|
8
| 0.1
| 0.4
| 0.2
|
sorrow
|
1
| 0.5
| 0.5
| 0.5
|
longing
|
2
| 0.5
| 0.5
| 0.5
|
longing
|
3
| 0.5
| 0.5
| 0.5
|
longing
|
4
| 0.1
| 0.8
| 0.45
|
tension
|
5
| 0.5
| 0.5
| 0.5
|
longing
|
6
| 0.3
| 0.6
| 0.5
|
annoyed
|
7
| 0.7
| 0.6
| 0.6
|
interest
|
8
| 0.5
| 0.5
| 0.5
|
longing
|
9
| 0.9
| 0.6
| 0.6
|
amusement
|
10
| 0.3
| 0.6
| 0.3
|
worry
|
11
| 0.15
| 0.7
| 0.6
|
resentment
|
12
| 0.183
| 0.883
| 0.283
|
fear
|
13
| 0.3
| 0.6
| 0.5
|
annoyed
|
14
| 0.85
| 0.4
| 0.7
|
relief
|
1
| 0.5
| 0.5
| 0.5
|
longing
|
2
| 0.5
| 0.5
| 0.5
|
longing
|
3
| 0.55
| 0.95
| 0.6
|
astonishment
|
4
| 0.2
| 0.95
| 0.4
|
anger
|
5
| 0.05
| 0.7
| 0.1
|
despair
|
1
| 0.3
| 0.6
| 0.3
|
worry
|
2
| 0.5
| 0.5
| 0.5
|
longing
|
3
| 0.6
| 0.55
| 0.45
|
longing
|
4
| 0.5
| 0.5
| 0.5
|
longing
|
5
| 0.75
| 0.6
| 0.7
|
curiosity
|
6
| 0.2
| 0.9
| 0.3
|
fear
|
1
| 0.483
| 0.667
| 0.433
|
longing
|
2
| 0.54
| 0.66
| 0.54
|
longing
|
3
| 0.45
| 0.6
| 0.55
|
irritation
|
4
| 0.443
| 0.736
| 0.464
|
impatience
|
5
| 0.225
| 0.65
| 0.4
|
envy
|
6
| 0.15
| 0.5
| 0.3
|
hurt
|
7
| 0.75
| 0.525
| 0.75
|
curiosity
|
8
| 0.693
| 0.607
| 0.543
|
interest
|
9
| 0.664
| 0.529
| 0.486
|
sympathy
|
10
| 0.3
| 0.6
| 0.3
|
worry
|
1
| 0.35
| 0.6
| 0.55
|
irritation
|
2
| 0.662
| 0.475
| 0.562
|
absorption
|
3
| 0.65
| 0.5
| 0.6
|
absorption
|
4
| 0.167
| 0.9
| 0.333
|
fear
|
5
| 0.45
| 0.6
| 0.55
|
irritation
|
6
| 0.55
| 0.55
| 0.5
|
longing
|
7
| 0.183
| 0.667
| 0.467
|
bitterness
|
8
| 0.5
| 0.5
| 0.5
|
longing
|
1
| 0.5
| 0.5
| 0.5
|
longing
|
2
| 0.35
| 0.731
| 0.537
|
impatience
|
3
| 0.375
| 0.95
| 0.45
|
anger
|
4
| 0.51
| 0.72
| 0.64
|
anticipation
|
5
| 0.4
| 0.595
| 0.485
|
irritation
|
6
| 0.267
| 0.6
| 0.283
|
worry
|
7
| 0.5
| 0.5
| 0.5
|
longing
|
1
| 0.542
| 0.621
| 0.537
|
longing
|
2
| 0.75
| 0.49
| 0.7
|
trust
|
3
| 0.393
| 0.729
| 0.407
|
impatience
|
4
| 0.588
| 0.525
| 0.55
|
longing
|
1
| 0.2
| 0.5
| 0.325
|
disappointment
|
2
| 0.25
| 0.8
| 0.3
|
nervousness
|
3
| 0.328
| 0.694
| 0.406
|
envy
|
4
| 0.5
| 0.5
| 0.5
|
longing
|
5
| 0.3
| 0.6
| 0.3
|
worry
|
1
| 0.5
| 0.5
| 0.5
|
longing
|
2
| 0.2
| 0.586
| 0.307
|
guilt
|
3
| 0.45
| 0.533
| 0.433
|
confusion
|
4
| 0.5
| 0.5
| 0.5
|
longing
|
5
| 0.7
| 0.512
| 0.637
|
absorption
|
1
| 0.65
| 0.5
| 0.6
|
absorption
|
2
| 0.213
| 0.675
| 0.425
|
jealousy
|
3
| 0.2
| 0.95
| 0.4
|
anger
|
4
| 0.483
| 0.533
| 0.383
|
confusion
|
1
| 0.2
| 0.8
| 0.4
|
tension
|
2
| 0.35
| 0.6
| 0.45
|
annoyed
|
End of preview. Expand
in Data Studio
Try the PV Peak/Valley Explorer
🔗 PV Radar (Beta) Space: https://huggingface.co/spaces/jsisonou/narrative-engine-pv-radar-beta
Use this dataset’s sample files to test:
- Curve Mode: upload
book_curve.scene.csv→ Run- Text Mode: paste one scene per line → Run
You’ll getpv_pred(per-scene labels),arc_summary(global peak/valley), and score curves.
Assistive only; human-in-the-loop. No model weights or training recipes are exposed.
⚠️ This repository is no longer maintained.
👉 Please visit the new repository: Narrative Engine Emotion (5c)
Open access (no fee) for academic & non-commercial research. For extended
columns (conf, plot_break), request no-fee access to the 7c tier
→ 7c Core (No-fee, Access Request)
| Tier | Columns | Access |
|---|---|---|
| 5c | scene_idx, valence, arousal, dominance, label | Open (no fee) |
| 7c | 5c + conf, plot_break | No-fee (access request) |
Try the PV Peak/Valley Explorer
🔗 PV Radar (Beta) Space: https://huggingface.co/spaces/jsisonou/narrative-engine-pv-radar-beta
Use this dataset’s sample files to test:
- Curve Mode: upload
book_curve.scene.csv→ Run- Text Mode: paste one scene per line → Run
You’ll getpv_pred(per-scene labels),arc_summary(global peak/valley), and score curves.
Assistive only; human-in-the-loop. No model weights or training recipes are exposed.
What’s inside (5 columns)
scene_idx— integer index (monotonic)valence— [-1.0, 1.0]arousal— [0.0, 1.0]dominance— [0.0, 1.0]label— {joy, sadness, anger, fear, disgust, surprise, trust, anticipation}
No raw narrative text is included.
Sample (CSV)
scene_idx,valence,arousal,dominance,label
1,0.12,0.55,0.48,anticipation
2,-0.42,0.64,0.51,fear
3,0.58,0.47,0.46,joy
Files
data/v1_0/vol01/ep001/5c_curve.csv— dataset (5 columns)schema/public_contract.schema.json— optional machine-readable interfaceLICENSE.txt— JsisOn License (ARR, research-only, non-commercial)
Licensing
- License: JsisOn License (ARR) — non-commercial research/review only
- Prohibited without explicit permission: redistribution, commercial use, training of general-purpose foundation models
- Contact: [email protected] (collaboration/permission requests)
Citation
If you use this dataset in academic work, please cite:
@dataset{batalstone_emotion_5c_free_arr_2025, author = {Liia Black}, title = {Webnovel Narrative Emotion — 5c Free (Research-Only, ARR)}, year = {2025}, publisher = {JsisOn OÜ} }
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