Crowdsourcing Emotions in Music Domain

Çano, Erion; Morisio, Maurizio

doi:10.5121/ijaia.2017.8403

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…They reduce the space into a categorical 2D model with 4 music mood classes, highly compliant with the popular model of Russell [30]. Viability and effectiveness of social tags and other crowdsourcing alternatives for creating labeled datasets is also discussed in [4]. In music domain, we use MoodyLyricsPN, a datasets of English song texts constructed utilizing a lexicon-based approach.…”

Section: Sentiment Analysis Of Song Lyricsmentioning

confidence: 99%

Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey

Çano¹,

Morisio²

2019

Preprint

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This work investigates the role of factors like training method, training corpus size and thematic relevance of texts in the performance of word embedding features on sentiment analysis of tweets, song lyrics, movie reviews and item reviews. We also explore specific training or post-processing methods that can be used to enhance the performance of word embeddings in certain tasks or domains. Our empirical observations indicate that models trained with multithematic texts that are large and rich in vocabulary are the best in answering syntactic and semantic word analogy questions. We further observe that influence of thematic relevance is stronger on movie and phone reviews, but weaker on tweets and lyrics. These two later domains are more sensitive to corpus size and training method, with Glove outperforming Word2vec. "Injecting" extra intelligence from lexicons or generating sentiment specific word embeddings are two prominent alternatives for increasing performance of word embedding features.

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Section: Sentiment Analysis Of Song Lyricsmentioning

confidence: 99%

Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey

Çano¹,

Morisio²

2019

Preprint

Self Cite

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“…Such annotations could be conducted by subjective test, but it usually result in a heavy load on time consumption and labor cost [9], which is not tractable with large-scale datasets such as those seen in MIR. As an alternative source of annotation, increasing interest has been shown in crowdsourcing resources [10].…”

Section: Introductionmentioning

confidence: 99%

Music Social Tags Representation in Dimensional Emotion Models

Ferguson

2020

2020 IEEE Intl Conf on Parallel &Amp; Distributed Processing With Applications, Big Data &Amp; Cloud Computing, Sustainable Com

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Much research has sought to recognize and retrieve music on the basis of emotion labels. These labels are usually obtained from either subjective test or social tags. Researchers use social tags usually either by grouping tags to emotion categories or clusters directly, or by mapping tags to dimensional quadrants simply. Few research work have undertaken semantic analysis on social tags for projecting them into a dimensional emotion space, especially based on recent neural word embedding techniques using large-scale datasets. In this paper, we propose an effective solution to analyse music tag information and represent them in a 2-dimension emotion plane without limiting the corpus to contain only emotion terms. In our solution, we apply neural word embedding methods for tag representation, including Skipgram, Continuous Bag-Of-Words (CBOW) and Global Vectors (GloVe). In our experiment, we compare these methods with traditional Latent Semantic Analysis (LSA) model based on Procrustes Analysis evaluation metrics. The results shows that neural tag embedding methods outperform LSA and represent tags with high approximations with classic circumplex emotion definitions.

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