著者

山田 康輔 笹野 遼平 武田 浩一

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.35, no.4, pp.B-K22_1-12, 2020-07-01 (Released:2020-07-01)

参考文献数

20

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It has been reported that a person’s remarks and behaviors reflect the person’s personality. Several recent studies have shown that textual information of user posts and user behaviors such as liking and reblogging the specific posts are useful for predicting the personality of Social Networking Service (SNS) users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of using textual information with user behaviors for personality prediction. We focus on the personality diagnosis website and make a large dataset on SNS users and their personalities by collecting users who posted the personality diagnosis on Twitter. Using this dataset, we work on personality prediction as a set of binary classification tasks. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors and the performance of prediction is strongly affected by the number of the user behaviors, which were incorporated into the prediction. We also show that user behavior information is crucial for predicting the personality of users who do not post frequently.

著者

山田 康輔

出版者

一般社団法人 言語処理学会

雑誌

自然言語処理 (ISSN:13407619)

巻号頁・発行日

vol.28, no.4, pp.1325-1330, 2021 (Released:2021-12-15)

参考文献数

6

被引用文献数

1

著者

山田 康輔 笹野 遼平 武田 浩一

雑誌

2023年度 人工知能学会全国大会(第37回)

巻号頁・発行日

2023-04-06

著者

山田 康輔 笹野 遼平 武田 浩一

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.35, no.4, pp.B-K22_1-12, 2020

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<p>It has been reported that a person's remarks and behaviors reflect the person's personality. Several recent studies have shown that textual information of user posts and user behaviors such as liking and reblogging the specific posts are useful for predicting the personality of Social Networking Service (SNS) users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of using textual information with user behaviors for personality prediction. We focus on the personality diagnosis website and make a large dataset on SNS users and their personalities by collecting users who posted the personality diagnosis on Twitter. Using this dataset, we work on personality prediction as a set of binary classification tasks. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors and the performance of prediction is strongly affected by the number of the user behaviors, which were incorporated into the prediction. We also show that user behavior information is crucial for predicting the personality of users who do not post frequently.</p>

著者

山田 康輔 笹野 遼平 武田 浩一

雑誌

研究報告自然言語処理(NL) (ISSN:21888779)

巻号頁・発行日

vol.2020-NL-244, no.5, pp.1-6, 2020-06-26

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本研究では,大規模コーパスからのフレーム知識獲得において,コーパスから収集された動詞の文脈を考慮することの有用性を検証する.具体的には,FrameNet および PropBank において 2 種類以上のフレームを喚起する動詞に着目し,それらの動詞が喚起するフレームの違いを ELMo や BERT に代表される文脈化単語埋め込みがどのくらい捉えているかを,各用例の文脈化単語埋め込みのクラスタリング結果とそれらに付与されたフレームを比較することにより調査する.