open access

https://hdl.handle.net/2115/63969

Automatically annotating a five-billion-word corpus of Japanese blogs for sentiment and affect analysis

英語

2014-01

Elsevier

Computer Speech & Language

28(1)

38-55

This paper presents our research on automaticannotation of a five-billion-word corpus ofJapanese blogs with information on affect andsentiment. We first perform a study in emotionblog corpora to discover that there has beenno large scale emotion corpus available forthe Japanese language. We choose the largestblog corpus for the language and annotate itwith the use of two systems for affect analysis:ML-Ask for word- and sentence-levelaffect analysis and CAO for detailed analysisof emoticons. The annotated informationincludes affective features like sentencesubjectivity (emotive/non-emotive) or emotionclasses (joy, sadness, etc.), useful in affectanalysis. The annotations are also generalizedon a 2-dimensional model of affect to obtaininformation on sentence valence/polarity(positive/negative) useful in sentiment analysis.The annotations are evaluated in severalways. Firstly, on a test set of a thousand sentencesextracted randomly and evaluated byover forty respondents. Secondly, the statisticsof annotations are compared to other existingemotion blog corpora. Finally, the corpus isapplied in several tasks, such as generation ofemotion object ontology or retrieval of emotionaland moral consequences of actions.

学術雑誌論文

Accepted Manuscript

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

© 2014, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

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