improving music mood classification using lyrics, audio and social tags

The 186 words were then grouped into 49 groups using the synset structure in WordNet.
Tags in each group were synonyms according to WordNet. After that, the two experts further
merged several tag groups which were deemed musically similar. For instance, the group of
(“cheer up,” “cheerful”) was merged with (“jolly,” “rejoice”); (“melancholic,” “melancholy”)
was merged with (“sad,” “sadness”). This resulted in 34 tag groups, each representing a mood
category for this dataset.
Finally, the author manually screened a number of tags that did not exactly match words in
WordNet-Affect but were most frequently applied to the songs in the dataset. Some of those tags
had exactly the same meaning as matched words in WordNet-Affect and thus were added into
corresponding categories. For instance, “sad song” and “feeling sad” were added into the
category of (“sad,” “sadness”); “mood: happy” and “happy songs” were added into the category
of (“happy,” “happiness”). In addition, there were some very popular tags with affect meanings
in the music domain but were not included in WordNet-Affect, such as “mellow” and “upbeat.”
The experts recommended including these tags in the categories of the same meaning. For
example, “mellow” was added to the (“calm,” “quiet”) category, and “upbeat” was added to the
category of (“gleeful,” “high spirits”).
For the classification experiments, each category should have enough samples to build
classification models. Thus, categories with fewer than 30 songs were dropped, resulting in 18
mood categories containing 135 tags. These categories and their member tags were then
validated for reasonableness by a number of native English speakers. Table 5.3 lists the
categories, their member tags and number of songs in each category (see next subsection).
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