improving music mood classification using lyrics, audio and social tags

1) Tempo: also called “rhythmic periodicity,” is estimated from both a spectral analysis of
each band of the spectrogram and the assessment of autocorrelation in the amplitude
envelope extracted from the audio.
2) Beat Histogram: a histogram representing distribution of tempi in a musical excerpt.
Usually the most prominent peak corresponds to the best tempo match. Experiments
often use several properties of the beat histogram such as the bpm (beats per minute)
values of the two highest peaks and the sum of all histogram bins, etc.
2.3.2 Text-based Music Mood Classification
Very recently, several studies on music mood classification have been conducted using only
music lyrics (He et al., 2008; Hu, Chen, & Yang, 2009b). He et al. (2008) compared traditional
bag-of-words features in unigrams, bigrams, trigrams and their combinations, as well as three
feature representation models (i.e., Boolean, absolute term frequency and tfidf weighting). Their
results showed that the combination of unigram, bigram and trigram tokens with tfidf weighting
performed the best, indicating that higher-order bag-of-words features captured more semantics
useful for mood classification. Hu et al. (2009b) moved beyond the bag-of-words lyric features
and extracted features based on an affective lexicon translated from the Affective Norms for
English Words (ANEW) (Bradley & Lang, 1999). The datasets used in both studies were
relatively small: the dataset in He et al. (2008) contained 1,903 songs in only two mood
categories, “love” and “lovelorn,” while Hu et al. (2009b) classified 500 Chinese songs into four
mood categories derived from Russell’s arousal-valence model.
From a different angle, Bischoff, Firan, Nejdl, and Paiu (2009a) tried to use social tags to
predict mood and theme labels of popular songs. The authors designed the experiments as a tag
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