=Paper= {{Paper |id=Vol-1388/latebreaking_paper1 |storemode=property |title=Personality & Emotional States: Understanding Users' Music Listening Needs |pdfUrl=https://ceur-ws.org/Vol-1388/latebreaking_paper1.pdf |volume=Vol-1388 |dblpUrl=https://dblp.org/rec/conf/um/FerwerdaST15 }} ==Personality & Emotional States: Understanding Users' Music Listening Needs== https://ceur-ws.org/Vol-1388/latebreaking_paper1.pdf

Personality & Emotional States: Understanding
Users’ Music Listening Needs

Bruce Ferwerda, Markus Schedl, and Marko Tkalcic

Department of Computational Perception, Johannes Kepler University,
Altenberger Str. 69, A-4040 Linz, Austria
bruce.ferwerda@jku.at

Abstract. Music plays an important part in people’s lives to regulate
their emotions throughout the day. We conducted an online user study
to investigate how the emotional state relates to the use of emotionally
laden music. We found among 359 participants that they in general pre-
fer emotionally laden music that correspond with their emotional state.
However, when looking at personality traits, different patterns emerged.
We found that when in a negative emotional state, those who scored high
on openness, extraversion, and agreeableness tend to cheer themselves up
with happy music, while those who scored high on neuroticism tend to
increase their worry with sad music. With our results we show general
patterns of music usage, but also individual differences. Our results con-
tribute to the improvement of applications such as recommender systems
in order to provide tailored recommendations based on users’ personality
and emotional state.

Keywords: Personality; Emotions; Music.

1 Introduction
We experience emotions in every facet of our life (e.g., during decision making,
thinking, creativity), and our behavior is influenced by the emotional state we
are in [16]. To regulate our emotional states, we rely on 162 different methods
where listening to music is the second most used method [9].
Within an emotion regulation method, we can adopt different strategies,
such as, changing, enhancing, or maintaining our emotional states. Previous re-
search has found that the preferred strategy is based on individual differences
(e.g., [11]). For example, some people prefer to be cheered up when feeling sad,
while others would like to stay in this emotional state a bit longer. Research has
shown that composers are able to effectively express the intended emotion of
their song to their audience [13], and that music is able to induce bona fide emo-
tions [16]. The ability of music to express and induce different kind of emotions
makes it well suited to support emotion regulation.
Current music applications that feature the ability for users to listen to music
that fits their emotional state, assume that they want to listen to music in line
with how they feel. However, since people adopt different emotion regulation
2 Ferwerda et al.

strategies, they may not always desire music which is similar with their emotional
state. Hence, in order to recommend the most appropriate music for users and
their current emotional state, understanding music listening needs on a general
as well as on an individual level is needed.
With this work we seek to expand the understanding of how one’s emo-
tional state relates to the preferred (emotionally laden) music. Music is known
to regulate emotions, but it is not known how emotional states relate to differ-
ent types of emotionally laden music preferences, nor how preference differences
breakdown on an individual level by looking at personality traits.
This leads us to the following research questions:

1. How do emotional states relate to emotionally laden music preferences?
2. How do personality traits relate to emotionally laden music preferences?

An online user study was conducted where participants were asked to rate
different emotionally laden music pieces on the listening likelihood based on their
current emotional state. Among 359 participants we found that the emotional
state is related to the use of emotionally laden music. Furthermore, individual
differences were identified based on personality traits.
We continue with the related work, method, findings, and discussion.

2 Related work

Ample research has investigated the effects of music and the importance of it in
everyday life. For example, Thompson and Robitaille found that composers are
effective in transferring the intended emotion of the music pieces to their audi-
ence [13], indicating that people perform well at interpreting music emotions.
People are not only good at recognizing emotions in music, but music is also
able to induce emotions in such a way that it is used as experimental stimuli
(e.g., [16]). Others have investigated how people use music. Parkinson and Tot-
terdell surveyed affect-regulation strategies, and found that music is used as a
common means [9]. Although the effects and usages of music has been extensively
investigated, it is striking that to our knowledge, no research has focused on how
emotionally laden music is used to regulate which emotion and how differences
exist on an individual level. With this work we try to answer these questions.

3 Method

Procedure. We developed an online experiment to get insights into the rela-
tionship between emotional states and emotionally laden music (Figure 1). In
this experiment, participants were put in an emotional state and were asked to
rate different emotionally laden music pieces on the listening likelihood, based on
their emotional state. Participants were recruited (N =382) through Amazon Me-
chanical Turk. Participation was restricted to those located in the United States,
and with a very good reputation. Several comprehension-testing questions were
Understanding Users’ Music Listening Needs 3

A) Inducing an B) Rating of emotionally C) Concluding
Emotionally emotional state Emotionally laden music pieces questionnaires
laden laden
film clips music

Film Emotional 5 different Annotating Likelihood BFI & demo.
clip state check music pieces music pieces questions Questionnaires

Fig. 1. User study work flow. A) We induced and checked participants’ emotional state,
then B) let them annotate emotionally laden music and asked the likelihood of listening
to it, and C) asked to fill in the personality (BFI) questionnaire and demographics.

used to filter out fake and careless entries. This left us with 359 completed and
valid responses. Gender (174 men and 185 women) and age (range 19-68, median
31) information indicated an adequate distribution.
Participants were informed that an emotional state is going to be induced
and were given a consent form. The study started with an example to familiar-
ize participants with the study. To induce an emotion, we used the film clips
presented in Table 1. The film clip of Hannah and her Sisters was always shown
in the example to provide a constant (neutral) baseline stimulus [5]. For the ac-
tual study, we randomly assigned the remaining film clips. A short synopsis was
provided before playback to increase involvement, and improve understanding
of the film clips’ content [15]. In line with the procedure of Hewig et al., we
asked participants at the end of the film clip to indicate how they were feeling
(by selecting an emotion from the set as seen in Table 1), and not what they
thought the film clip was suppose to express [7]. In the next step, five emotionally
laden music pieces, from within and between the emotion categories (Table 2),
were randomly presented. Participants were asked to annotate the emotion they
thought the music piece was trying to express (by selecting an emotion from the
set as seen in Table 2), and the likelihood (5-point Likert scale; never-always) of
listening to such emotionally laden music, considering their (reported) emotional
state. The study ended with the personality questionnaire and demographics.

Materials. For our stimuli we relied on existing materials that have been tested
in prior studies. To induce an emotional state, we used film clips as they are the
most powerful emotion elicitation technique in a controlled environment [10].
Hewig et al. designed film clips to induce an emotional state without sound, and
categorized them based on Ekman’s emotion categorization (anger, fear, happy,
surprise, disgust, and sad; Table 1) [7]. Using muted stimuli allowed us to control
for conflicts with our music pieces in the annotation step of the study. 1
We used the emotionally laden music pieces created by Eerola and Vuoskoski.
They defined music pieces based on the emotional value they bear, based on
Ekman’s emotion categorization. Film soundtracks were used as they are created
with the purpose to mediate powerful emotional cues. Additionally, as they are

1
As the surprise emotion only lasts seconds [2], we decided not to include this.
4 Ferwerda et al.

Table 1. The film clips with their length, Table 2. The albums with the track num-
and emotion. ber, length, and emotion.
Film Clip Length (s) Emotion Album (track number) Length (s) Emotion
Hannah and her Sisters 92 Neutral The Rainmaker (3) 18 Happy
Crimes and Misdemeanors 63 Neutral Batman (18) 20 Happy
All the President’s Men 65 Neutral Lethal Weapon 3 (8) 14 Anger
An Officer and a Gentleman 111 Happy The Rainmaker (7) 15 Anger
When Harry met Sally 149 Happy Batman Returns (5) 16 Fear
Witness 91 Anger JFK (8) 14 Fear
My Bodyguard 236 Anger The English Patient (18) 25 Sad
Silence of the Lambs 202 Fear Running Scared (15) 19 Sad
Halloween 208 Fear Shine (10) 20 Tender
An Officer and a Gentlemen 101 Sad
Pride & Prejudice (1) 16 Tender
The Champ 171 Sad
Maria’s Lovers 58 Disgust
Pink Flamingos 29 Disgust

instrumental, they are relatively neutral in terms of musical preferences and
(artist) familiarity (Table 2) [1]. 2
We assessed personality traits with the widely used 44-item Big Five Inven-
tory (BFI; 5-point Likert scale; disagree strongly - agree strongly [8]), which
describes personality in terms of openness to experience, conscientiousness, ex-
traversion, agreeableness, and neuroticism.

4 Findings

The analyses were done based on participants’ reported emotional state after
the film clip was shown, not on the intended induced emotion by the film clip.
The distribution of the reported emotional states were as follows: happy (n=55),
neutral (n=82), anger (n=62), disgust (n=56), fear (n=79), and sad (n=61).
An initial one-way multivariate analysis of variance (MANOVA) was con-
ducted to test the relationship between emotionally laden music pieces and
emotional states. A significant MANOVA effect was obtained (Wilks’ Lambda =
.523, F (25, 1164.24) = 8.89, p <.001) with a moderate effect size (η 2 =.13). The
homogeneity of variance assumption was tested for all the emotionally laden mu-
sic pieces. Levene’s F test showed that the music pieces depicting the emotional
states happy and tender do not meet the requirement of p >.05. None of the
largest standard deviations of the two pieces were more than four times the size
of the corresponding smallest, suggesting that follow-up ANOVAs are robust.
Post-hoc tests (Tukey HSD) were performed to examine individual mean
difference comparisons across the six emotional states and the five emotionally
laden music pieces. The results reported here were compared against a neutral
emotional state and were all statistically significant (p <.05). Results revealed
that in general, participants preferred happy and tender music when in a neutral
emotional state. However, in a angry or disgusted state, participants preferred
2
Eerola and Vuoskoski [1] replaced disgust with tender, as disgust is rarely expressed
by music. Music depicting surprise was omitted due to lack of statistical significance.
Understanding Users’ Music Listening Needs 5

angry or fearful music. They also preferred sad music when they were feeling
sad. Additionally, participants indicated a dislike of happy and tender music
when they felt angry, fearful, or disgusted.
Follow-up ANOVAs were conducted to test for individual differences. Results
revealed that in a neutral emotional state, participants who scored high on agree-
ableness tend to listen more to happy (F (1, 19.27) =16.12, p <.001) and tender
(F (1, 11.89) =11.40, p <.005) music. When participants felt happy, the ones who
scored high on openness tend to listen more to happy music (F (1, 9.02) =8.85,
p <.05). Participants who scored high on neuroticism and felt disgusted tend to
listen more to sad music (F (1, 12.73) =8.47, p <.005). Lastly when participants
felt sad, and scored high on extraversion (F (1, 16.95) =9.96, p <.005), agree-
ableness (F (1, 13.29) =7.81, p <.05), or openness (F (1, 16.29) =9.57, p <.005),
they tend to listen more to happy music.

5 Discussion

Our data show that the emotional state influences the (emotionally laden) mu-
sic people listen to. In a neutral emotional state, happy and tender music is
consumed more frequently. Additionally, findings indicate that people in general
prefer emotionally laden music that is in line with their emotional state. Angry
and fearful music is preferred when feeling angry or disgusted, whereas preference
for happy and tender music decreases for these emotional states. Additionally,
we found an increase of sad music in a sad state.
Taking personality traits into account, individual differences emerged. One of
our findings showed that those who scored high on openness, extraversion, and
agreeableness are more inclined to listen to happy music when they are feeling
sad. In other words; they are trying to cheer themselves up with happy music.
On the other hand, we found that those who are neurotic try to maintain their
negative emotional state by listening to more sad songs.
In order to provide personalized music recommendations, we identified im-
portant individual differences that deviate from the notion that users desire to
listen to music which is in line with their emotional state. By using personality to
identify individual differences, we join the emergent interest of personality-based
personalized systems. Several solutions have already been proposed to incorpo-
rate personality (e.g., [3, 4, 14]). For example, adaptation of the user interface
of music recommender systems based on personality traits [4]. Also the extrac-
tion of emotion from social media is starting to establish (e.g., Twitter feeds [6]).
Given our results there are several implications to consider. Music systems could
anticipate the next song in the queue, or provide a list of recommendations, based
on the user’s current emotional state. This allows the system to better serve the
user’s music listening needs, and support their emotion regulation strategy.
Although we relied on self-report measures to assess emotions (emotion in-
duction as well as music annotations) through an online platform, over 85% of
the responses were in line with the original classifications that have been exten-
sively tested priorly [1, 7]. This suggests that the used methods were effective.
6 Ferwerda et al.

Our results focused on individual differences of music preferences based on
general emotional states. However, as Tamir and Ford [12] noted, emotion regula-
tion strategies depend not only on individual differences, but also on the context
that people are situated in. They found that people want to experience unpleas-
ant emotions to attain certain instrumental benefits. That is, people want to
feel bad when they expect it to give them benefits. For example, in confronting
situations. We will address the influence of context in future work.

6 Acknowledgments
This research is supported by the Austrian Science Fund: P25655, and the EU
FP7/2013-2016 through the PHENICX project under grant agreement 601166.

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