Natural Language Processing for Music Knowledge Discovery
Table 1: Number of albums by genre with information from the different sources in the albums reviews dataset.
['[BOLD] Genre', '[BOLD] Amazon', '[BOLD] MusicBrainz']
In this section, we put forward an integration procedure for enriching with music-related information a large dataset of Amazon customer reviews McAuley2015a, with semantic metadata obtained from MusicBrainz. The initial dataset of Amazon customer reviews provides millions of review texts together with additional information such as overall rating (between 0 to 5), date of publication, or creator id. Each review is associated to a product and, for each product, additional metadata is also provided, namely, Amazon product id, list of similar products, price, sell rank, and genre categories. From this initial dataset, we selected the subset of products categorized as CDs & Vinyls, which also fulfill the following criteria. First, considering that the Amazon taxonomy of music genres contains 27 labels in the first hierarchy level, and about 500 in total, we obtain a music-relevant subset and select 16 of the 27 which really define a music style and discard for instance region categories (e.g., World Music) and other categories specifically non-related to a music style (e.g., Soundtrack, Miscellaneous, Special Interest), function-oriented categories (Karaoke, Holiday & Wedding), or categories whose albums might also be found under other categories (e.g., Opera & Classical Vocal, Broadway & Vocalists). We compiled albums belonging only to one of the 16 selected categories, i.e., no multi-label. Note that the original dataset contains not only reviews about CDs and Vinyls, but also about music DVDs and VHSs. Since these are not strictly speaking music audio products, we filter out those products also classified as "Movies & TV". Finally, since products classified as Classical and Pop are substantially more frequent in the original dataset, we compensate this unbalance by limiting the number of albums of any genre to 10,000. After this preprocessing, the dataset amounts to a total of 65,566 albums and 263,525 customer reviews.