Using machine learning analysis to interpret the relationship between music emotion and lyric features

Xu, Liang; Sun, Zaoyi; Wen, Xin; Huang, Zhengxi; Chao, Chiju; Xu, Liuchang

doi:10.7717/peerj-cs.785

Cited by 10 publications

(3 citation statements)

References 67 publications

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“…For the machine learning algorithm, this work applied the random forest classification (RFC) algorithm. As RFC has shown good performance in classification tasks [ 48 , 49 ], we can easily interpret the constructed RFC models by calculating the feature importance [ 50 , 51 , 52 , 53 , 54 ]. The predictive effect of each model was evaluated by the tenfold cross-validation technique.…”

Section: Methodsmentioning

confidence: 99%

Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Wang

et al. 2022

IJERPH

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Consumer financial fraud has become a serious problem because it often causes victims to suffer economic, physical, mental, social, and legal harm. Identifying which individuals are more likely to be scammed may mitigate the threat posed by consumer financial fraud. Based on a two-stage conceptual framework, this study integrated various individual factors in a nationwide survey (36,202 participants) to construct fraud exposure recognition (FER) and fraud victimhood recognition (FVR) models by utilizing a machine learning method. The FER model performed well (f1 = 0.727), and model interpretation indicated that migration status, financial status, urbanicity, and age have good predictive effects on fraud exposure in the Chinese context, whereas the FVR model shows a low predictive effect (f1 = 0.565), reminding us to consider more psychological factors in future work. This research provides an important reference for the analysis of individual differences among people vulnerable to consumer fraud.

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Section: Methodsmentioning

confidence: 99%

Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Wang

et al. 2022

IJERPH

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“…Xu L., et al explored the influence of lyrics on the perceived emotions of music [1]. It utilizes linguistic inquiry and word count (LIWC) technology to analyze 2,372 Chinese songs.…”

Section: Related Workmentioning

confidence: 99%

Lyrically Yours: A Mobile Application for Automated Music Therapy through Lyric Analysis Utilizing Natural Language Processing and Machine Learning

Yang,

Sun

2024

Software Engineering &Amp; Trends

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Music plays a vital role in therapy, offering a unique avenue for emotional expression and connection. This proposed project seeks to enhance the effectiveness of music therapy by leveraging natural language processing (NLP) techniques and machine learning to provide personalized song recommendations based on both emotional and narrative elements within lyrics, moving beyond traditional approaches that focus solely on emotional categorization [4]. By utilizing keyword matching algorithms, the project expands the scope of song selection, allowing users to explore music beyond predefined emotional categories [5]. The proposed system integrates with Firebase for efficient data storage and retrieval, while the Flutter framework facilitates the development of a user-friendly mobile application interface [6]. Through this interdisciplinary approach, the project endeavors to offer an accessible and automated music therapy experience.

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“…Melody and lyrics, two distinct human cognitive abilities, are typically combined in music to convey emotions. L Xu et al [ 20 ] investigated the effects of LIWC-based lyrical features on emotions conveyed by music using Linguistic Inquiry and Word Count (LIWC) technology to extract lyric features from 2372 Chinese songs. The proportion of words conveying negative emotions was inversely related to the perceived valence of music.…”

Section: Related Workmentioning

confidence: 99%

Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

Modran

Chamunorwa

Ursuțiu

et al. 2023

Sensors

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Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.

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Using machine learning analysis to interpret the relationship between music emotion and lyric features

Cited by 10 publications

References 67 publications

Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Integrating Individual Factors to Construct Recognition Models of Consumer Fraud Victimization

Lyrically Yours: A Mobile Application for Automated Music Therapy through Lyric Analysis Utilizing Natural Language Processing and Machine Learning

Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

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