improving music mood classification using lyrics, audio and social tags

3.1.3 Cleaning up Social Tags by Human Experts
Human expertise is applied as a final step to ensure the quality of the mood-related term list.
This research consulted two human experts who were respected MIR researchers with a music
background and native English speakers. In this dissertation research where human experts were
consulted with regard to music mood terms, the two experts worked together at the same place
and time. They manually examined the terms and discussed discrepant opinions with each other
until they reached the same decisions. In this way, all terms were considered by both experts and
all decisions were made by the best judgments of both experts. In this particular task of cleaning
up non-mood tags, the experts examined the remaining 236 terms. They first identified and
removed tags with music meanings that did not involve an affective aspect (e.g., “trance” and
“beat”). Then, they removed words with ambiguous meanings. For example, “chill” can mean
“to calm down” or “depressing,” but social tags do not provide enough context to disambiguate
the term. Finally, they also identified and removed additional evaluation words that were not
included in General Inquirer, such as “fascinating” and “dazzling.” After this step, there
remained 136 mood-related terms.
3.1.4 Grouping Mood-related Social Tags
As a means of solving the synonym problem of social tags, the mood-related tags are
organized into groups such that synonyms are merged together into one group. Tags in each
resultant group then collectively define a mood category. This step again uses WordNet-Affect.
WordNet is a natural resource for identifying synonyms, because it organizes words into synsets.
Words in the same synset are synonyms from a linguistic point of view. Moreover, WordNet-
Affect also links each non-noun synset (verb, adjective and adverb) with the noun synset from
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