Emotion Classification of Song Lyrics using Bidirectional LSTM Method with GloVe Word Representation Weighting

Abdillah, Jiddy; Asror, Ibnu; Wibowo, Yanuar Firdaus Arie

doi:10.29207/resti.v4i4.2156

Cited by 18 publications

(14 citation statements)

References 12 publications

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“…This outcome is a result of the exploitation of both lyric and audio data and making one concentrated prediction for each song by its segments. Additionally, in Table 7 we compare our model (BERT + CNN) with other models that utilize the same dataset (MoodyLyrics) as us [ 12 , 46 , 47 , 48 , 49 ]. We should also mention that our final model outperforms previous approaches that use different sources of data, specifically our model achieves 94.32% F 1 Score while Malheiro’s model [ 14 ] achieves 88.4% F 1 Score .…”

Section: Resultsmentioning

confidence: 99%

Multi-Modal Song Mood Detection with Deep Learning

Pyrovolakis

Tzouveli

Stamou

2022

Sensors

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The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. In 2018, Delbouys et al. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes, {happy, angry, sad, relaxed}. The result of this work leads to a uniform prediction of the mood that represents a music track and has usage in many applications.

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Section: Resultsmentioning

confidence: 99%

Multi-Modal Song Mood Detection with Deep Learning

Pyrovolakis

Tzouveli

Stamou

2022

Sensors

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“…In this study, we employ Multi-task setup, using XLNet as the base architecture for classification of emotions and evaluate the performance of our model on several datasets that have been organized by emotional connotations solely based on lyrics. We demonstrate superior performance of our transformer-based approach compared to RNN-based approach [9,2]. In addition, we propose a robust methodology for extracting lyrics for a song.…”

Section: Related Workmentioning

confidence: 89%

“…In recent years the use of pre-trained models like GloVe [32], ELMO [33], transformers [10,37] are fast gaining importance for large text corpus has shown impressive results in downstream several NLP tasks. Authors in [9,2] perform emotion classification using lyrics by applying RNN model on top of word-level embedding. The MoodyLyrics dataset [5] was used by [2] who report an impressive F 1 -score of 91.00%.…”

Section: Related Workmentioning

confidence: 99%

“…Authors in [9,2] perform emotion classification using lyrics by applying RNN model on top of word-level embedding. The MoodyLyrics dataset [5] was used by [2] who report an impressive F 1 -score of 91.00%. Recurrent models like LSTMs work on Markov's principle, where information from past steps goes through a sequence of computations to predict a future state.…”

Section: Related Workmentioning

confidence: 99%

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Transformer-based approach towards music emotion recognition from lyrics

Agrawal,

Shanker,

Alluri

2021

Preprint

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The task of identifying emotions from a given music track has been an active pursuit in the Music Information Retrieval (MIR) community for years. Music emotion recognition has typically relied on acoustic features, social tags, and other metadata to identify and classify music emotions. The role of lyrics in music emotion recognition remains underappreciated in spite of several studies reporting superior performance of music emotion classifiers based on features extracted from lyrics. In this study, we use the transformer-based approach model using XLNet as the base architecture which, till date, has not been used to identify emotional connotations of music based on lyrics. Our proposed approach outperforms existing methods for multiple datasets. We used a robust methodology to enhance web-crawlers' accuracy for extracting lyrics. This study has important implications in improving applications involved in playlist generation of music based on emotions in addition to improving music recommendation systems.

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“…It is considered one of the breakthroughs in deep learning. Studies in [5], [6], [7] suggested the Word2Vec approach to extract text features, while others suggested Glove [8]. Nevertheless, both approaches are context-independent, and they could not catch all semantic information such as Out-Of-Vocabulary (OOV) and some opposite word pairs.…”

Section: Introductionmentioning

confidence: 99%

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

Kurniasih¹,

Manik²

2022

IJACSA

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Due to highly unstructured and noisy data, analyzing society reports in written texts is very challenging. Classifying informal text data is still considered a difficult task in natural language processing since the texts could contain abbreviated words, repeating characters, typos, slang, et cetera. Therefore, text preprocessing is commonly performed to remove the noises and make the texts more structured. However, we argued that most tasks of preprocessing are no longer required if suitable word embeddings approach and deep neural network (DNN) architecture are correctly chosen. This study investigated the effects of text preprocessing in fine-tuning a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using various DNN architectures such as multilayer perceptron (MLP), long short-term memory (LSTM), bidirectional long-short term memory (Bi-LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU). Various experiments were conducted using numerous learning rates and batch sizes. As a result, text preprocessing had insignificant effects on most models such as LSTM, Bi-LSTM, and CNN. Moreover, the combination of BERT embeddings and CNN produced the best classification performance.

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Emotion Classification of Song Lyrics using Bidirectional LSTM Method with GloVe Word Representation Weighting

Cited by 18 publications

References 12 publications

Multi-Modal Song Mood Detection with Deep Learning

Multi-Modal Song Mood Detection with Deep Learning

Transformer-based approach towards music emotion recognition from lyrics

On the Role of Text Preprocessing in BERT Embedding-based DNNs for Classifying Informal Texts

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