ragegun

Lexica based text analysis.

As crates.io is a pain in the ass, this module will download the lexica from GitHub at build time.

API

Age (default feature, age)

mod ragegun;

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

Gender (default feature, gender)

mod ragegun;

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

Distress (default feature, distress)

mod ragegun;

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

PERMA (default feature, 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,
    // }
}

References

Age & gender prediction

• 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.

Personal distress prediction

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

Prospection Lexicon: Temporal Orientation

• 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..

Dark Triad

• 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.

Empathic Concern

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

EmoLex / NRC Word-Emotion Association Lexicon

• 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).

Affect Intensity / NRC Emotion Intensity Lexicon

• Mohammad, S. (2018). Word Affect Intensities. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018).

NRC Valence, Arousal, Dominance Lexicon

• Mohammad, S. (2018). Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. In Proceedings of The Annual Conference of the Association for Computational Linguistics (ACL).

PERMA (Positive Emotions, Engagement, Relationships, Meaning, Accomplishment)

• Schwartz, & Ungar, L. (2016). Predicting Individual Well-Being Through the Language of Social Media. Pacific Symposium on Biocomputing, 21, 516-527.

Sources

• WWBP (Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported)
• NRC Word-Emotion Association Lexicon (EmoLex)
• NRC Emotion Intensity Lexicon (NRC-EIL)
• NRC Valence, Arousal, and Dominance (NRC-VAD)