improving music mood classification using lyrics, audio and social tags

mixed with regard to its usefulness. This dissertation research strives to find out whether and
how the ANEW scores can help classify text sentiment in the lyrics domain.
Besides scores in the three dimensions, for each word ANEW also provides the standard
deviation of the scores in each dimension given by the human subjects. Therefore there are six
values associated with each word in ANEW. For the lyrics of each song, means and standard
deviations for each of these values are calculated for words included in ANEW, which results in
12 features.
As the number of words in the original ANEW is probably too few to have at least one word
included in each of the songs in the experiment dataset, the ANEW word list is expanded using
WordNet. WordNet, as mentioned before, is an English lexicon with marked linguistic
relationships among word senses. It is organized by synsets such that word senses in one synset
are essentially synonyms. Hence, ANEW is expanded by including all words in WordNet that
share the same synset with a word in ANEW and giving these words the same ANEW scores as
the one in ANEW. Again, word senses are not differentiated since ANEW only presents word
forms without specifying which sense is used. After expansion, there are 6,732 words in the
expanded ANEW which covers all songs in the experiment dataset. That is, every song has nonzero
values in the 12 dimensions. This feature type is denoted as “ANEW.”
Like the words from General Inquirer, the 6,732 words in the expanded ANEW can be seen
as a lexicon of affect-related words. Together with the 1,586 unique words in the latest version of
WordNet-Affect, the expanded ANEW forms an affect lexicon of 7,756 unique words. This set
of words are used to build bag-of-words features under the aforementioned four representation
models. This feature type is denoted as “Affect-lex.”
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