Xu Liang, Sun Zaoyi, Wen Xin, Huang Zhengxi, Chao Chi-Ju, Xu Liuchang
Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
College of Education, Zhejiang University of Technology, Hangzhou, China.
PeerJ Comput Sci. 2021 Nov 15;7:e785. doi: 10.7717/peerj-cs.785. eCollection 2021.
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.
旋律和歌词反映了人类两种独特的认知能力，它们在音乐中通常相互结合以传达情感。尽管心理学家和计算机科学家在揭示音乐结构与音乐感知情感之间的关联方面取得了显著进展，但歌词的特征相对较少被讨论。本研究使用语言查询与字数统计（LIWC）技术提取了2372首中文歌曲中的歌词特征，调查了基于LIWC的歌词特征对音乐感知唤醒度和效价的影响。首先，相关性分析表明，例如，音乐的感知唤醒度与歌词总字数和平均每句字数呈正相关，与与过去和洞察力相关的词汇比例呈负相关。音乐的感知效价与负面情绪词汇的比例呈负相关。其次，我们将音频和歌词特征作为输入来构建音乐情感识别（MER）模型。随机森林回归的结果显示，对于感知效价的识别模型，添加歌词特征可以显著提高仅使用音频特征的模型的预测效果；对于感知唤醒度的识别模型，歌词特征几乎没有用处。最后，通过计算特征重要性来解释MER模型，我们观察到音频特征在感知唤醒度和感知效价的识别模型中都起决定性作用。与唤醒度识别模型中歌词特征的无用性不同，一些歌词特征，如与悲伤、积极情绪和试探性相关的词汇使用频率，在效价识别模型中发挥了重要作用。
