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EXPLORING CUSTOMER REVIEWS FOR MUSIC GENRE CLASSIFICATION AND EVOLUTIONARY STUDIES Sergio Oramas1, Luis Espinosa-Anke2, Aonghus Lawlor3, Xavier Serra1, Horacio Saggion2 1Music Technology Group, Universitat Pompeu Fabra 2TALN Group, Universitat Pompeu Fabra 3Insight Centre for Data Analytics, University College of Dublin sergio.oramas, luis.espinosa, xavier.serra, horacio.saggion @upf.edu, [email protected] { } ABSTRACT dataset of Amazon customer reviews [18,19], with seman- tic and acoustic metadata obtained from MusicBrainz 1 In this paper, we explore a large multimodal dataset of and AcousticBrainz 2 , respectively. MusicBrainz (MB) is about 65k albums constructed from a combination of Ama- a large open music encyclopedia of music metadata, whist zon customer reviews, MusicBrainz metadata and Acous- AcousticBrainz (AB) is a database of music and audio de- ticBrainz audio descriptors. Review texts are further en- scriptors, computed from audio recordings via state-of-the- riched with named entity disambiguation along with po- art Music Information Retrieval algorithms [26]. In addi- larity information derived from an aspect-based sentiment tion, we further extend the semantics of the textual content analysis framework. This dataset constitutes the corner- from two standpoints. First, we apply an aspect-based sen- stone of two main contributions: First, we perform ex- timent analysis framework [7] which provides specific sen- periments on music genre classification, exploring a va- timent scores for different aspects present in the text, e.g. riety of feature types, including semantic, sentimental and album cover, guitar, voice or lyrics. Second, we perform acoustic features. These experiments show that modeling Entity Linking (EL), so that mentions to named entities semantic information contributes to outperforming strong such as Artist Names or Record Labels are linked to their bag-of-words baselines. Second, we provide a diachronic corresponding Wikipedia entry [24]. study of the criticism of music genres via a quantitative This enriched dataset, henceforth referred to as Multi- analysis of the polarity associated to musical aspects over modal Album Reviews Dataset (MARD), includes affec- time. Our analysis hints at a potential correlation between tive, semantic, acoustic and metadata features. We benefit key cultural and geopolitical events and the language and from this multidimensional information to carry out two evolving sentiments found in music reviews. experiments. First, we explore the contribution of such features to the Music Genre classification task, consisting in, given a song or album review, predict the genre it be- 1. INTRODUCTION longs to. Second, we use the substantial amount of infor- With the democratisation of Internet access, vast amounts mation at our disposal for performing a diachronic analysis of information are generated and stored in online sources, of music criticism. Specifically, we combine the metadata and thus there is great interest in developing techniques retrieved for each review with their associated sentiment for processing this information effectively [27]. The Mu- information, and generate visualizations to help us investi- sic Information Retrieval (MIR) community is sensible to gate any potential trends in diachronic music appreciation this reality, as music consumption has undergone signifi- and criticism. Based on this evidence, and since music cant changes recently, especially since users are today just evokes emotions through mechanisms that are not unique one click away from millions of songs [4]. This context re- to music [16], we may go as far as using musical infor- sults in the existence of large repositories of unstructured mation as means for a better understanding of global af- knowledge, which have great potential for musicological fairs. Previous studies argue that national confidence may studies or tasks within MIR such as music recommenda- be expressed in any form of art, including music [20], and tion. in fact, there is strong evidence suggesting that our emo- In this paper, we put forward an integration proce- tional reactions to music have important and far-reaching dure for enriching with music-related information a large implications for our beliefs, goals and actions, as members of social and cultural groups [1]. Our analysis hints at a potential correlation between the language used in music c Sergio Oramas1, Luis Espinosa-Anke2, Aonghus reviews and major geopolitical events or economic fluctu- Lawlor3, Xavier Serra1, Horacio Saggion2. Licensed under a Creative ations. Finally, we argue that applying sentiment analysis Commons Attribution 4.0 International License (CC BY 4.0). Attribu- to music corpora may be useful for diachronic musicolog- tion: Sergio Oramas1, Luis Espinosa-Anke2, Aonghus Lawlor3, Xavier Serra1, Horacio Saggion2. “Exploring Customer Reviews for Music ical studies. Genre Classification and Evolutionary Studies”, 17th International So- 1 http://musicbrainz.org/ ciety for Music Information Retrieval Conference, 2016. 2 http://acousticbrainz.org 150 Proceedings of the 17th ISMIR Conference, New York City, USA, August 7-11, 2016 151 2. RELATED WORK 3. MULTIMODAL ALBUM REVIEWS DATASET MARD contains texts and accompanying metadata origi- One of the earliest attempts on review genre classification nally obtained from a much larger dataset of Amazon cus- is described in [15], where experiments on multiclass genre tomer reviews [18, 19]. The original dataset provides mil- classification and star rating prediction are described. Sim- lions of review texts together with additional information ilarly, [14] extend these experiments with a novel approach such as overall rating (between 0 to 5), date of publica- for predicting usages of music via agglomerative cluster- tion, or creator id. Each review is associated to a product ing, and conclude that bigram features are more infor- and, for each product, additional metadata is also provided, mative than unigram features. Moroever, part-of-speech namely Amazon product id, list of similar products, price, (POS) tags along pattern mining techniques are applied sell rank and genre categories. From this initial dataset, in [8] to extract descriptive patterns for distinguishing neg- we selected the subset of products categorized as CDs & ative from positive reviews. Additional textual evidence is Vinyls, which also fulfill the following criteria. First, con- leveraged in [5], who consider lyrics as well as texts re- sidering that the Amazon taxonomy of music genres con- ferring to the meaning of the song, and used for training a tains 27 labels in the first hierarchy level, and about 500 kNN classifier for predicting song subjects (e.g. war, sex in total, we obtain a music-relevant subset and select 16 of or drugs). the 27 which really define a music style and discard for in- stance region categories (e.g. World Music) and other cate- In [23], a dataset of music reviews is used for album gories non specifically related to a music style (e.g. Sound- rating prediction by exploiting features derived from sen- track, Miscellaneous, Special Interest), function-oriented timent analysis. First, music-related topics are extracted categories (Karaoke, Holiday & Wedding) or categories (e.g. artist or music work), and this topic information is whose albums might also be found under other categories further used as features for classification. One of the most (e.g. Opera & Classical Vocal, Broadway & Vocalists). thorough works on music reviews is described in [28]. We compiled albums belonging only to one of the 16 se- It applies Natural Language Processing (NLP) techniques lected categories, i.e. no multiclass. Note that the original such as named entity recognition, text segmentation and dataset contains not only reviews about CDs and Vinyls, sentiment analysis to music reviews for generating texts but also about music DVDs and VHSs. Since these are not explaining good aspects of songs in recommender systems. strictly speaking music audio products, we filter out those In the line of review generation, [9] combine text analysis products also classified as ”Movies & TV”. Finally, since with acoustic descriptors in order to generate new reviews products classified as Classical and Pop are substantially from the audio signal. Finally, semantic music information more frequent in the original dataset, we compensate this is used in [29] to improve topic-wise classification (album, unbalance by limiting the number of albums of any genre artist, melody, lyrics, etc.) of music reviews using Sup- to 10,000. After this preprocessing, MARD amounts to a port Vector Machines. This last approach differs from ours total of 65,566 albums and 263,525 customer reviews. A in that it enriches feature vectors by taking advantage of breakdown of the number of albums per genre is provided ad-hoc music dictionaries, while in our case we take ad- in Table 1. vantage of Semantic Web resources. Genre Amazon MusicBrainz AcousticBrainz Alternative Rock 2,674 1,696 564 As for sentiment classification of text, there is abundant Reggae 509 260 79 Classical 10,000 2,197 587 literature on the matter [21], including opinions, reviews R&B 2,114 2,950 982 and blog posts classification as positive or negative. How- Country 2,771 1,032 424 Jazz 6,890 2,990 863 ever, the impact of emotions has received considerably less Metal 1,785 1,294 500 attention in genre-wise text classification. We aim at bridg- Pop 10,000 4,422 1701 New Age 2,656 638 155 ing this gap by exploring aspect-level sentiment analysis Dance & Electronic 5,106 899 367 features. Rap & Hip-Hop 1,679 768 207 Latin Music 7,924 3,237 425 Rock 7,315 4,100 1482 Finally, concerning studies on the evolution of music Gospel 900 274 33 genres, these have traditionally focused on variation in au- Blues 1,158 448 135 Folk 2,085 848 179 dio descriptors, e.g. [17], where acoustic descriptors of Total 66,566 28,053 8,683 17,000 recordings between 1960 and 2010 are analyzed. Descriptors are discretized and redefined as descriptive Table 1: Number of albums by genre with information words derived from several lexicons, which are subse- from the different sources in MARD quently used for topic modeling.
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