| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
The purpose of this study is to extract three kinds of affective information from sentences such as tweets: emotions of the writers who wrote the sentences, impressions which readers felt from reading the sentences, and the writers' emotions which the readers estimated by reading the sentences.
First, we conduct questionnaire surveys, and analyze the relationships between tweets and emotions and impressions extracted from the tweets. And then, we formulate these relationships using multivariate analysis methods and machine learning methods, resulting in high accuracy and robustness. We have developed a method that can extract the emotions and impressions of tweets.
Note that, in this study, ten kinds of emotions of "joy, love, relief, sad, dislike, fear, anger, embarrassment, uplifting, and surprise" and eight kinds of impressions of "aggressive/unpleasant, negative, pleasant, pleasant/happy, positive, heartwarming, annoying, and scary" are used.
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