Song Emotion Detection Based on Arousal-Valence from Audio and Lyrics Using Rule Based Method

Rachman, Fika Hastarita; Samo, Riyanarto; Fatichah, Chastine

doi:10.1109/icicos48119.2019.8982519

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…NLP technology has been widely used to analyze the emotions expressed or perceived in texts, such as book reviews (Zhang, Tong, & Bu, 2019), opinions on social media platforms (Xu, Li et al, 2020), movie reviews (Kaur & Verma, 2017;Lu & Wu, 2019), party statements (Haselmayer & Jenny, 2017), and song lyrics (Rachman, Samo, & Fatichah, 2019). Knowledge-based approaches and machine learning-based approaches are two common approaches used for emotional analysis (Liu & Chen, 2015).…”

Section: Nlp-based Lyric Featuresmentioning

confidence: 99%

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

2021

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Section: Nlp-based Lyric Featuresmentioning

confidence: 99%

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

2021

View full text Add to dashboard Cite

Identification of Non-Vehicle and Vehicle Density at Intersections Based on GPS Data

Aziz,

Rakhman,

Pratama

et al. 2024

2024 International Conference on Information Technology and Computing (ICITCOM)

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Using machine learning analysis to interpret the relationship between music emotion and lyric features

Sun

Wen

et al. 2021

PeerJ Computer Science

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Melody and lyrics, reflecting two unique human cognitive abilities, are usually combined in music to convey emotions. Although psychologists and computer scientists have made considerable progress in revealing the association between musical structure and the perceived emotions of music, the features of lyrics are relatively less discussed. Using linguistic inquiry and word count (LIWC) technology to extract lyric features in 2,372 Chinese songs, this study investigated the effects of LIWC-based lyric features on the perceived arousal and valence of music. First, correlation analysis shows that, for example, the perceived arousal of music was positively correlated with the total number of lyric words and the mean number of words per sentence and was negatively correlated with the proportion of words related to the past and insight. The perceived valence of music was negatively correlated with the proportion of negative emotion words. Second, we used audio and lyric features as inputs to construct music emotion recognition (MER) models. The performance of random forest regressions reveals that, for the recognition models of perceived valence, adding lyric features can significantly improve the prediction effect of the model using audio features only; for the recognition models of perceived arousal, lyric features are almost useless. Finally, by calculating the feature importance to interpret the MER models, we observed that the audio features played a decisive role in the recognition models of both perceived arousal and perceived valence. Unlike the uselessness of the lyric features in the arousal recognition model, several lyric features, such as the usage frequency of words related to sadness, positive emotions, and tentativeness, played important roles in the valence recognition model.

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Song Emotion Detection Based on Arousal-Valence from Audio and Lyrics Using Rule Based Method

Cited by 3 publications

References 12 publications

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

Peer Review #1 of "Using machine learning analysis to interpret the relationship between music emotion and lyric features (v0.2)"

Identification of Non-Vehicle and Vehicle Density at Intersections Based on GPS Data

Using machine learning analysis to interpret the relationship between music emotion and lyric features

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