@inproceedings{kondo-etal-2025-text,
    title = "Text Normalization for {J}apanese Sentiment Analysis",
    author = "Kondo, Risa  and
      Teramen, Ayu  and
      Kajikawa, Reon  and
      Horiguchi, Koki  and
      Kajiwara, Tomoyuki  and
      Ninomiya, Takashi  and
      Hayashi, Hideaki  and
      Nakashima, Yuta  and
      Nagahara, Hajime",
    editor = "Bak, JinYeong  and
      Goot, Rob van der  and
      Jang, Hyeju  and
      Buaphet, Weerayut  and
      Ramponi, Alan  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.wnut-1.16/",
    pages = "149--157",
    ISBN = "979-8-89176-232-9",
    abstract = "We manually normalize noisy Japanese expressions on social networking services (SNS) to improve the performance of sentiment polarity classification.Despite advances in pre-trained language models, informal expressions found in social media still plague natural language processing.In this study, we analyzed 6,000 posts from a sentiment analysis corpus for Japanese SNS text, and constructed a text normalization taxonomy consisting of 33 types of editing operations.Text normalization according to our taxonomy significantly improved the performance of BERT-based sentiment analysis in Japanese.Detailed analysis reveals that most types of editing operations each contribute to improve the performance of sentiment analysis."
}