Lyrically Speaking: Exploring the Link Between Lyrical Emotions, Themes and Depression Risk

• URL: http://arxiv.org/abs/2408.15575v1
• Date: Wed, 28 Aug 2024 07:00:19 GMT
• Title: Lyrically Speaking: Exploring the Link Between Lyrical Emotions, Themes and Depression Risk
• Authors: Pavani Chowdary, Bhavyajeet Singh, Rajat Agarwal, Vinoo Alluri,
• Abstract summary: Specific lyrical themes and emotions may intensify existing negative states in listeners. Individuals at risk for depression prefer songs with lyrics associated with low valence and low arousal. This study opens up the possibility of an approach to assessing depression risk from the digital footprint of individuals.
• Score: 2.0784944581469205
• License:
• Abstract: Lyrics play a crucial role in affecting and reinforcing emotional states by providing meaning and emotional connotations that interact with the acoustic properties of the music. Specific lyrical themes and emotions may intensify existing negative states in listeners and may lead to undesirable outcomes, especially in listeners with mood disorders such as depression. Hence, it is important for such individuals to be mindful of their listening strategies. In this study, we examine online music consumption of individuals at risk of depression in light of lyrical themes and emotions. Lyrics obtained from the listening histories of 541 Last.fm users, divided into At-Risk and No-Risk based on their mental well-being scores, were analyzed using natural language processing techniques. Statistical analyses of the results revealed that individuals at risk for depression prefer songs with lyrics associated with low valence and low arousal. Additionally, lyrics associated with themes of denial, self-reference, and ambivalence were preferred. In contrast, themes such as liberation, familiarity, and activity are not as favored. This study opens up the possibility of an approach to assessing depression risk from the digital footprint of individuals and potentially developing personalized recommendation systems.

Related papers

• Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions [2.6571678272335717]
  This study investigates the impact of discussion topic as context on linguistic markers and emotional expression in depression. Our sentiment analysis revealed a broader range of emotional intensity in depressed individuals, with both higher negative and positive sentiments than controls.
  arXiv  Detail & Related papers  (2024-05-28T11:19:39Z)
• Joint sentiment analysis of lyrics and audio in music [1.2349562761400057]
  In automatic analysis, the actual audio data is usually analyzed, but the lyrics can also play a crucial role in the perception of moods. We first evaluate various models for sentiment analysis based on lyrics and audio separately. The corresponding approaches already show satisfactory results, but they also exhibit weaknesses.
  arXiv  Detail & Related papers  (2024-05-03T10:42:17Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• CauESC: A Causal Aware Model for Emotional Support Conversation [79.4451588204647]
  Existing approaches ignore the emotion causes of the distress. They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers. We propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes.
  arXiv  Detail & Related papers  (2024-01-31T11:30:24Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Exploring Musical, Lyrical, and Network Dimensions of Music Sharing Among Depression Individuals [14.293723126727485]
  Social media has become an important platform for individuals navigating through depression. This study explores the differences in music preferences between individuals diagnosed with depression and non-diagnosed individuals.
  arXiv  Detail & Related papers  (2023-10-17T20:08:43Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Affective Idiosyncratic Responses to Music [63.969810774018775]
  We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
  arXiv  Detail & Related papers  (2022-10-17T19:57:46Z)
• How Much do Lyrics Matter? Analysing Lyrical Simplicity Preferences for Individuals At Risk of Depression [0.0]
  We compare lyrical simplicity trends for users grouped as being at risk (At-Risk) of depression from those that are not (No-Risk) Our findings reveal that At-Risk individuals prefer songs with greater information content (lower Compressibility) on average, especially for songs characterised as Sad. At-Risk individuals also have greater variability of Absolute Information Content across their listening history.
  arXiv  Detail & Related papers  (2021-09-15T11:41:20Z)
• Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue [67.89143112645556]
  We provide a NICO robot with the ability to learn the associations between a perceived auditory stimulus and an emotional expression. NICO is able to do this for both individual subjects and specific stimuli, with the aid of an emotion-driven dialogue system. The robot is then able to use this information to determine a subject's enjoyment of perceived auditory stimuli in a real HRI scenario.
  arXiv  Detail & Related papers  (2021-03-05T20:55:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.