Datasets:

eduhk-compling
/

GroupG_Project

Tasks:

Automatic Speech Recognition

Text Classification

Audio Classification

Modalities:

Formats:

Languages:

Size:

Tags:

emotion-recognition

Libraries:

License:

Dataset card Data Studio Files Files and versions

DennisQIU commited on 30 days ago

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@@ -2,15 +2,19 @@
 language:
 - zh
 task_categories:
 - text-classification
-- token-classification
 tags:
 - esports
-- multimodal
-- sentiment-analysis
 license: cc-by-4.0
 ---
 # KPL Esports Linguistic Dataset
-## Dataset Description
 This dataset introduces a multimodal labeled dataset including 65 minutes and 24 seconds of commentary from three dynamic matches in the King Pro League (KPL) of the game "Honor of Kings": the 2021 Autumn Semifinals, 2025 Finals, and 2025 Summer Finals. This dataset was collected by extracting AI-generated, time-aligned transcripts from recording videos on BiliBili Platform, which combines essential manually modification to ensure accuracy. It features comprehensive manual annotations, including speaker recognition, screen event descriptions, domain-specific terminology (OOV) extraction, and emotion grading. Provided in CSV format, this dataset addresses the scarcity of high-intensity, domain-specific spoken language resources, offering significant potential for multimodal sentiment analysis and esports-related Natural Language Processing (NLP) research.

 language:
 - zh
 task_categories:
+- automatic-speech-recognition
 - text-classification
+- audio-classification
 tags:
 - esports
+- linguistics
+- emotion-recognition
+- kpl
+size_categories:
+- 1K<n<10K
 license: cc-by-4.0
 ---
 # KPL Esports Linguistic Dataset
+## Abstract
 This dataset introduces a multimodal labeled dataset including 65 minutes and 24 seconds of commentary from three dynamic matches in the King Pro League (KPL) of the game "Honor of Kings": the 2021 Autumn Semifinals, 2025 Finals, and 2025 Summer Finals. This dataset was collected by extracting AI-generated, time-aligned transcripts from recording videos on BiliBili Platform, which combines essential manually modification to ensure accuracy. It features comprehensive manual annotations, including speaker recognition, screen event descriptions, domain-specific terminology (OOV) extraction, and emotion grading. Provided in CSV format, this dataset addresses the scarcity of high-intensity, domain-specific spoken language resources, offering significant potential for multimodal sentiment analysis and esports-related Natural Language Processing (NLP) research.