ragegun

基于词汇的文本分析。

由于 crates.io 令人痛苦，此模块将在构建时从 GitHub 下载词汇。

API

年龄（默认功能，age）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");
    
    dbg!(rg.age()); // value like 30.00
}

性别（默认功能，gender）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");
    
    rg.gender() // value like GenderInterpretation::Female
}

压力（默认功能，distress）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");
    
    dbg!(rg.distress()); // value like 1.00
}

PERMA（默认功能，perma）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");
    
    dbg!(rg.perma());
    
    // PERMAAnalysis {
    //  positive_emotion: Negative,
    //  engagement: Negative,
    //  relationships: Postive,
    //  meaning: Negative,
    //  accomplishment: Negative,
    // }
}

EmoLex（emolex_all_languages，20MB+）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");

    dbg!(rg.emolex_all_languages());

    // EmoLexEmotions {
    // anger: Neutral,
    // anticipation: Neutral,
    // disgust: Neutral,
    // fear: Neutral,
    // joy: Neutral,
    // negative: Neutral,
    // positive: Neutral,
    // sadness: Neutral,
    // surprise: Neutral,
    // trust: Neutral,
    // }
}

EmoLex（emolex_english，~2MB）

mod ragegun;

fn main() {
    let rg = ragegun::RageGun::new("foo");

    dbg!(rg.emolex_english());

    // EmoLexEmotions {
    // anger: Neutral,
    // anticipation: Neutral,
    // disgust: Neutral,
    // fear: Neutral,
    // joy: Neutral,
    // negative: Neutral,
    // positive: Neutral,
    // sadness: Neutral,
    // surprise: Neutral,
    // trust: Neutral,
    // }
}

参考文献

年龄 & 性别预测

• Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzynski, L., Ramones, S. M., Agrawal, M., Shah, A., Kosinski, M., Stillwell, D., Seligman, M. E., & Ungar, L. H. (2013). Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLOS ONE, 8(9), e73791.

个人压力预测

• João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, & Lyle Ungar (2019). Learning Word Ratings for Empathy and Distress from Document-Level User

展望词汇：时间取向

• Schwartz, H. A., Park, G., Sap, M., Weingarten, E., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., Berger, J., Seligman, M., & Ungar, L. (2015). Extracting Human Temporal Orientation from Facebook Language. NAACL-2015: Conference of the North American Chapter of the Association for Computational Linguistics..

• Park, G., Schwartz, H.A., Sap, M., Kern, M.L., Weingarten, E., Eichstaedt, J.C., Berger, J., Stillwell, D.J., Kosinski, M., Ungar, L.H. & Seligman, M.E. (2015). Living in the Past, Present, and Future: Measuring Temporal Orientation with Language. Journal of personality..

暗黑三联

• Preotiuc-Pietro, Daniel and Carpenter, Jordan and Giorgi, Salvatore and Ungar, Lyle, {CIKM} (2016). Studying the Dark Triad of Personality using Twitter Behavior. Proceedings of the 25th {ACM} Conference on Information and Knowledge.

同理心关注

• João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, & Lyle Ungar (2019). Learning Word Ratings for Empathy and Distress from Document-Level User

EmoLex / NRC 词-情感关联词汇表

• Mohammad, S., & Turney, P. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3), 436–465.

• Mohammad, S., & Turney, P. (2010). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text (pp. 26–34).

影响强度 / NRC 情感强度词汇表

• 穆罕默德，S. (2018). 单词情感强度。在《第11届语言资源与评估会议（LREC-2018）》论文集中。

NRC 估值、唤醒、支配词汇表

• 穆罕默德，S. (2018). 获取20,000个英语单词的可靠的人为估值：估值、唤醒和支配。在《计算语言学协会年度会议（ACL）》论文集中。

PERMA（积极情绪、参与度、关系、意义、成就感）

• 施瓦茨，Ungar，L. (2016)。通过社交媒体的语言预测个人幸福感。太平洋生物计算研讨会，21，516-527。

来源

• WWBP（Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported）
• NRC 单词-情感关联词汇表（EmoLex）
• NRC 情感强度词汇表（NRC-EIL）
• NRC 估值、唤醒和支配（NRC-VAD）