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A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain

Proceedings of the Thirteenth International Conference on Language Resources and Evaluation (LREC 2022)

DOI:10.63317/29rabiyatqxj

Abstract

We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, weak, medium, and strong) for each of the eight basic emotions by Plutchik (joy, sadness, anticipation, surprise, anger, fear, disgust, and trust) as well as sentiment polarity labels (strong positive, positive, neutral, negative, and strong negative). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer’s and reader’s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.

Details

Paper ID
lrec2022-main-759
Pages
pp. 7022-7028
BibKey
suzuki-etal-2022-japanese
Editor
N/A
Publisher
European Language Resources Association (ELRA)
ISSN
2522-2686
ISBN
79-10-95546-38-2
Conference
Thirteenth Language Resources and Evaluation Conference
Location
Marseille, France
Date
20 June 2022 25 June 2022

Authors

  • HS

    Haruya Suzuki

  • YM

    Yuto Miyauchi

  • KA

    Kazuki Akiyama

  • TK

    Tomoyuki Kajiwara

  • TN

    Takashi Ninomiya

  • NT

    Noriko Takemura

  • YN

    Yuta Nakashima

  • HN

    Hajime Nagahara

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