Search results

Genre classification of songs based on lyrics is a challenging task even for humans, however, state-of-the-art natural language processing has recently offered advanced solutions to this task. The purpose of this study is to advance the understanding and application of natural language processing and deep learning in the domain of music genre classification, while also contributing to the broader themes of global knowledge and communication, and sustainable preservation of cultural heritage.

The main contribution of this study is the development and evaluation of various machine and deep learning models for song genre classification. Additionally, we investigated the effect of different word embeddings, including Global Vectors for Word Representation (GloVe) and Word2Vec, on the classification performance. The tested models range from benchmarks such as logistic regression, support vector machine and random forest, to more complex neural network architectures and transformer-based models, such as recurrent neural network, long short-term memory, bidirectional long short-term memory and bidirectional encoder representations from transformers (BERT).

The authors conducted experiments on both English and multilingual data sets for genre classification. The results show that the BERT model achieved the best accuracy on the English data set, whereas cross-lingual language model pretraining based on RoBERTa (XLM-RoBERTa) performed the best on the multilingual data set. This study found that songs in the metal genre were the most accurately labeled, as their text style and topics were the most distinct from other genres. On the contrary, songs from the pop and rock genres were more challenging to differentiate. This study also compared the impact of different word embeddings on the classification task and found that models with GloVe word embeddings outperformed Word2Vec and the learning embedding layer.

This study presents the implementation, testing and comparison of various machine and deep learning models for genre classification. The results demonstrate that transformer models, including BERT, robustly optimized BERT pretraining approach, distilled bidirectional encoder representations from transformers, bidirectional and auto-regressive transformers and XLM-RoBERTa, outperformed other models.

The consequences of corporate corruption control (CCC) have either been investigated outside the firm (e.g. foreign direct investment inflows) or inside the firm (e.g. profitability). Yet prior research addresses these implications separately, treating them as distinct phenomena, ignoring questions at their intersection. However, corruption control can be leveraged to benefit both organizations (internally) and environments (externally). In line with open systems theory, this study aims to explore a ripple effect of corruption control not only inside organizations (efficiency through adoption of sustainable resource management practices) but also outside [community-centered corporate social performance (CSP)].

Using a longitudinal sample of multinational enterprises from Forbes list of “The World’s Largest Public Companies,” the authors use a cross-lagged panel design to provide clarity regarding causal effects.

Results confirm causal directionality and support the positive effect of corruption control on resource management and community CSP, contributing toward understanding implications at the organization–environment interface.

The authors examine both internal and external implications of CCC. The use of a cross-lagged design that is relatively novel to the management field allows to check for casual effects between CSP elements that were previously assumed to have reciprocal casual effects.