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<p>This paper presents a novel synthetic, automatically annotated dialogue-style dataset derived from narrative short stories, formatted as Speaker(Emotion): Dialogue. The corpus comprises 2,588 stories containing 27,074 dialogue utterances attributed to 2,582 unique characters, each annotated with one of six emotion categories (Joy, Sadness, Anger, Fear, Disgust, Surprise). Built from a Hugging Face story corpus, the dataset captures speaker attribution and emotional context across dialogues. To accurately identify speakers and their genders, we employed FastCoref with masked dialogue processing, followed by BookNLP for enhanced entity resolution. Emotional labels were assigned using a Bi-GRU-based emotion classifier, while representative story-level keywords were extracted using Mistral-7B via prompt engineering. The resulting dataset pairs keywords with fully-annotated emotional dialogues, enabling keyword-conditioned narrative generation. This dataset serves as a foundation for fine-tuning generative language models and was further used to generate narrations with emotional TTS using ElevenLabs, showcasing the practical utility of our annotations for emotionally expressive storytelling.</p>

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Narrative Dialogue Dataset: Speaker and Emotion Annotated Conversational Corpus

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Uzma Patil,
  • Sejal Hanmante,
  • Shivani Patil,
  • Aniket K. Shahade

摘要

This paper presents a novel synthetic, automatically annotated dialogue-style dataset derived from narrative short stories, formatted as Speaker(Emotion): Dialogue. The corpus comprises 2,588 stories containing 27,074 dialogue utterances attributed to 2,582 unique characters, each annotated with one of six emotion categories (Joy, Sadness, Anger, Fear, Disgust, Surprise). Built from a Hugging Face story corpus, the dataset captures speaker attribution and emotional context across dialogues. To accurately identify speakers and their genders, we employed FastCoref with masked dialogue processing, followed by BookNLP for enhanced entity resolution. Emotional labels were assigned using a Bi-GRU-based emotion classifier, while representative story-level keywords were extracted using Mistral-7B via prompt engineering. The resulting dataset pairs keywords with fully-annotated emotional dialogues, enabling keyword-conditioned narrative generation. This dataset serves as a foundation for fine-tuning generative language models and was further used to generate narrations with emotional TTS using ElevenLabs, showcasing the practical utility of our annotations for emotionally expressive storytelling.