Love Me, Love Me, Say (and Write!) that You Love Me:Enriching the WASABI Song Corpus with Lyrics Annotations
Table 2: Performance comparison of our different models. Precision (P), Recall (R) and f-score (F1) in %.
['[ITALIC] Model', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
On audio recordings, the Parental Advisory Label is placed in recognition of profanity and to warn parents of material potentially unsuitable for children. Nowadays, such labelling is carried out mainly manually on voluntary basis, with the drawbacks of being time consuming and therefore costly, error prone and partly a subjective task. We compared automated methods ranging from dictionary-based lookup to state-of-the-art deep neural networks to automatically detect explicit contents in English lyrics. More specifically, the dictionary-based methods rely on a swear word dictionary Dn which is automatically created from example explicit and clean lyrics. Then, we use Dn to predict the class of an unseen song text in one of two ways: (i) the Dictionary Lookup simply checks if a song text contains words from Dn. (ii) the Dictionary Regression uses BOW made from Dn as the feature set of a logistic regression classifier. In the Tf-idf BOW Regression the BOW is expanded to the whole vocabulary of a training sample instead of only the explicit terms. Our analysis revealed that some genres are highly overrepresented among the explicit lyrics. Inspecting the automatically induced explicit words dictionary reflects that genre bias. The dictionary of 32 terms used for the dictionary lookup method consists of around 50% of terms specific to the Rap genre, such as glock, gat, clip (gun-related), thug, beef, gangsta, pimp, blunt (crime and drugs). Finally, the terms holla, homie, and rapper are obviously no swear words, but highly correlated with explicit content lyrics.