LYRIC JUMPER: A LYRICS-BASED MUSIC EXPLORATORYWEB SERVICE BY MODELING LYRICS GENERATIVE PROCESS
Kosetsu Tsukuda
Keisuke Ishida
Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan{k.tsukuda, ksuke-ishida, m.goto}@aist.go.jp
ABSTRACT
Each artist has their own taste for topics of lyrics such as“love” and “friendship.” Considering such artist’s taste
brings new applications in music information retrieval:choosing an artist based on topics of lyrics and finding unfamiliar artists who have similar taste to a favorite artist.
Although previous studies applied latent Dirichlet allocation (LDA) to lyrics to analyze topics, LDA was not able
to capture the artist’s taste. In this paper, we propose a
topic model that can deal with the artist’s taste for topics
of lyrics. Our model assumes each artist has a topic distribution and a topic is assigned to each song according
to the distribution. Our experimental results using a realworld dataset show that our model outperforms LDA interms of the perplexity. By applying our model to estimate
topics of 147,990 lyrics by 3,722 artists, we implement a
web service called Lyric Jumper that enables users to explore lyrics based on the estimated topics. Lyric Jumper
provides functions such as artist’s topic taste visualizationand topic-similarity-based artist recommendation. We also
analyze operation logs obtained from 12,353 users on Lyric
Jumper and show the usefulness of Lyric Jumper especiallyin recommending topic-related phrases in lyrics.
1. INTRODUCTION
Different artists have different tastes in lyrics. Someartists tend to sing about “love,” while other artists tend
to sing about “friendship.” When listening to music, people choose artists according to not only musical audio content, such as music genre, mood, melody, vocal timbre, andrhythm, but also the topics of lyrics [2, 21]. However, thepotential of using the topics of lyrics has not yet been fully
exploited in the field of music information retrieval (MIR).
For example, it is difficult to choose an artist based on the
topics of their lyrics, find unfamiliar artists that are similar to the user’s favorite artist in terms of the topics of the
lyrics, and listen to a song that has the user’s favorite topic
of lyrics. The goal of this research is to achieve lyricsbased MIR that can leverage the topics of lyrics at bothartist and song levels.
One approach for lyrics-based MIR is to directly use thec Kosetsu Tsukuda, Keisuke Ishida, Masataka Goto.
⃝
Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Kosetsu Tsukuda, Keisuke Ishida,
Masataka Goto. “Lyric Jumper: A Lyrics-based Music Exploratory
Web Service by Modeling Lyrics Generative Process”, 18th International
Society for Music Information Retrieval Conference, 2017.

words in lyrics. Users input some words as a query [5, 27]or can find the same phrase in the lyrics of another song
while they are listening to music [9]. Another approach is
to use a topic model because it can deal with the underlying
meanings of lyrics. The topic is usually represented by a
distribution over the vocabulary, and the meaning of topics(e.g., “love” or “friendship”) is determined based on the
distribution. In lyrics-based MIR, it has been popular touse latent Dirichlet allocation (LDA) [1] as a topic model.
LDA models each song as a mixture of topics and assigns
a topic to each word in the song’s lyrics. Since LDA does
not take the set of songs of each artist into account, it is notable to capture the artist’s taste for topics of lyrics.
In light of the above, we propose a topic model thatconsiders the artist’s taste for topics of lyrics. In the lyrics
generative process of our model, each artist has a distribution over topics that reflects the artist’s taste for topics intheir lyrics. In addition, since it is common to decide thetheme for a song before starting to write its lyrics [4, 36],
our model assigns one topic to each song. That is, given a
topic k assigned to a song, topic k is also assigned to thewords in its lyrics. We also use the background word distribution because not all the words in lyrics are related tothe topic.
By using our proposed model, we implemented alyrics-based music exploratory web service, called Lyric
Jumper1 2 . Lyric Jumper aims to enable users to explore
lyrics and enjoy music in a more flexible way by considering songs’ topics. Our proposed model automatically assigns 1 of 20 topics for each song, where the 20 topics arealso automatically estimated by our model. Lyric Jumper
provides several topic-based functions such as visualization of the topic tendency for a given artist, artist ranking
based on topics, and artist recommendation based on the
topic distribution similarity.
Our main contributions in this paper are as follows.
• To the best of our knowledge, this is the first study
modeling a lyrics generative process by considering
the artist’s taste for topics of lyrics and assuming
each song has one topic. (Section 3)• We quantitatively evaluated our model by using areal-world song dataset provided by a lyrics distribution company. Our experimental results show thatour proposed model outperformed the conventional
LDA in terms of the perplexity. (Section 4)
1 https://lyric-jumper.petitlyrics.com
2 The demonstration video: https://youtu.be/5V9kHnelSAk• By using our proposed model, we implemented a
web service, called Lyric Jumper, that enables users
to search for songs based on the topics of their lyrics.
We also analyzed the search logs obtained from
more than 12,000 users and showed the impact of
Lyric Jumper on users’ search behavior. (Section 5)
2. RELATED WORK
Previous studies have used lyrics for various objectives
such as lyrics-to-audio alignment [7, 22, 35], analyzing
lyrics characteristics [8, 13, 16, 29, 34], accurately finding
lyrics [11, 19, 24], genre or mood classification [15, 25,
26, 38–40], songwriting support [30], and video generation [10]. This section describes more related studies interms of (1) lyrics-based music retrieval/browsing systems
and (2) topic-based lyrics analysis and applications.
2.1 Lyrics-Based Music Retrieval/Browsing Systems
Brochu and de Freitas [5] modeled music and text jointly
so that users can search song databases using music and/or
text as input. Müller et al. [27] automatically annotated audio recordings of a given song with its corresponding lyrics
and realized a query-by-lyrics retrieval system. When auser selects a query result, the system can directly navigate
to the corresponding matching positions within the audio.
Detecting songs from the user’s singing lyrics is also a popular research topic [14,37]. Fujihara et al. [9] proposed theconcept of a “Music Web” where songs were hyperlinked
to each other based on the phrases of lyrics. This enables
users to jump to the same phrase in the lyrics of anothersong by clicking a linked phrase while they are listening tomusic. Visualization is also a useful approach to browse amusic collection. SongWords [3], which is an application
for tabletop computers, displays a music collection on a
two-dimensional canvas based on self-organizing maps for
lyrics and tags. Lyricon [23] is a system that automaticallyselects and displays icons that match the word sequencesof lyrics so that users can intuitively understand the lyrics.
Although these studies directly use the words in lyrics,
we consider topics that are automatically estimated fromlyrics. Our approach has an advantage in that users can explore lyrics based on the underlying meanings of the lyrics.
2.2 Topic-Based Lyrics Analysis and Applications
Since a topic model can learn the underlying meanings oflyrics, it has been used in various studies, including lyrics
analysis [17, 31, 33], lyrics retrieval applications [32], anda music player [28]. In terms of lyrics analysis, Sharma
and Murty [33] analyzed the hidden sentimental structure
behind lyrics by using LDA and revealed that some of thedetected topics correspond to sentiments. Similarly, by applying LDA to rap lyrics, not only expected topics suchas “street life” and “religion” but also unexpected ones
such as “family/childhood” can be discovered [17]. Ren
et al. [31] tackled the problem of predicting the popularity of a music track by considering lyrics topics and foundthat more than half of the popular tracks are related to the
topic of “love.” Regarding applications, LyricsRadar [32]is a lyrics retrieval system that visualizes the topic ratio
for each song by using the topic radar chart and enables
users to find their favorite lyrics interactively. Nakano andGoto [28] presented a music playback interface LyricListPlayer that enables users to see word sequences of othersongs similar to the sequence currently being played back,where the similarity is computed based on the topic.
In these studies, LDA is used as a topic model, where
it is assumed that each song has a topic distribution anda topic is assigned to each word in the lyrics. We propose a new topic model that assumes each artist has a topic
distribution and a topic is assigned to each song. Since
our model outperforms LDA (see Section 4), there is thepotential for improving previous studies on lyrics analysis
and applications by using our model.
The study closest to ours is that of Kleedorfer et al. [18],who applied non-negative matrix factorization to lyrics forclustering them and manually labeled the cluster names.
Our study differs from theirs in that we consider the artist’s
taste for topics of lyrics, and this enables users to find theirfavorite lyrics based on the relationships between artists
and topics. Moreover, we not only propose a new model
but also implement a web service so that everyone can explore lyrics with a real world dataset.
3. MODEL AND INFERENCE
In this section, after summarizing the notations used in ourmodel in Section 3.1, we first describe LDA in Section 3.2
and then propose our model in Section 3.3.
3.1 Notations
Given a lyrics dataset, let A be the set of artists in thedataset. Let Ra be the number of songs of artist a ∈ A inathe dataset; then the set of a’s songs is given by {Sar }R
r=1 ,where Sar represents the rth song of a. Moreover, let Varbe the number of words in the lyrics of Sar ; then Sar canarbe represented by Sar = {varj }Vj=1, where varj is the jthword in Sar . Hence, the set of words of all artists’ lyrics isar Ragiven by D = {{{varj }Vj=1}r=1 }a∈A .
3.2 LDA (Latent Dirichlet Allocation)
When LDA is used as a generative process of lyrics, it is assumed that (1) each song has a distribution over topics, (2)a topic is assigned to each word in the song’s lyrics according to the distribution, and (3) a word is generated fromthe topic’s distribution over words. Figure 1(a) shows thegraphical model of LDA, where the shaded and unshaded
circles represent the observed and unobserved variables,
respectively. In the figure, K is the number of topics, θ isthe song-topic distribution, and ϕ is the topic-word distribution. We assume that θ and ϕ have Dirichlet priors of
α and β, respectively. The generative process of LDA is
described in Algorithm 1.
3.3 Artist’s Taste (AT) Model
Although previous studies reported the usefulness of applying LDA to lyrics [17, 28, 31–33], LDA does not takeartist information into account in the generative process.

(a)

(b)

Figure 1. Graphical models of (a) baseline LDA and (b)proposed artist’s taste (AT) model.
It is reasonable to assume that each artist has their own
taste for topics of lyrics. For example, one artist may tend
to sing lyrics related to the topic of “love,” while anotherartist may tend to sing lyrics related to the topic of “life.”
In light of the above, we propose a model that considers
the artist’s taste for topics. Figure 1(b) shows the graphical model of our proposed model. In our model, each artist
has a distribution over topics (θ). When people write lyrics,
the writer typically decides the theme (i.e., the topic) before starting to write the lyrics [4, 36]. Hence, we assume
each song has a topic z that is generated from θ. However,
not all of the words in the lyrics are related to the topic. For
example, although “thing” and “this” frequently appear inmany lyrics, usually these words do not represent a specific topic. To solve this problem, we use the idea of background words [6]. In Figure 1(b), ψ represents the background word distribution, where words that are not related
to any topic have high occurrence probabilities. Each artist
has a Bernoulli distribution λ that controls the weights ofinfluence for a song topic and background words. To be
more specific, when artist a chooses a word in a song, we
assume that the choice is influenced by the song topic withprobability λa0 (x = 0) and by background words withprobability λa1 (x = 1), where λa0 + λa1 = 1. Whenx = 0, a word is generated from the topic’s distribution
over words, while when x = 1, a word is generated fromthe background word distribution ψ. The generative process of the AT model is described in Algorithm 2.
3.4 Inference
To learn the parameters of our proposed model, we use
collapsed Gibbs sampling [12] to obtain samples of hidden variable assignment. Since we use a Dirichlet priorfor θ, ϕ, and ψ and a Beta prior for λ, we can analytically calculate the marginalization over the parameters. The marginalized joint distribution of D, latent varia
ables Z = {{zar }R
r=1 }a∈A , and latent variables X =Var Ra{{{xarj }j=1 }r=1 }a∈A is computed as follows:

Algorithm 1 LDA generative process
for each topic k ∈ {1, · · · , K} do
Draw ϕk ∼ Dirichlet(β)
end forfor each artist a in A dofor each song Sar do
Draw θar ∼ Dirichlet(α)
for each word varj in Sar do
Draw a topic zarj ∼ Multinomial (θar )Draw a word varj ∼ Multinomial (ϕzarj )end forend forend for
Algorithm 2 AT model generative process
for each topic k ∈ {1, · · · , K} do
Draw ϕk ∼ Dirichlet(β)
end forDraw ψ ∼ Dirichlet(γ)
for each artist a in A do
Draw θa ∼ Dirichlet(α)
Draw λa ∼ Beta(ρ).
for each song Sar do
Draw a topic zar ∼ Multinomial (θa )for each word varj in Sar do
Draw switch x ∼ Bernoulli (λa )if x = 0 thenDraw a word varj ∼ Multinomial (ϕzar )else if x = 1 thenDraw a word varj ∼ Multinomial (ψ)
end ifend forend forend forP (D, Z, X|α, β, γ, ρ)∫∫∫∫=P (D, Z, X|Θ, Φ, ψ, Λ)P (Θ|α)× P (Φ|β)P (ψ|γ)P (Λ|ρ)dΘdΦdψdΛ,(1)where Θ = {θa }a∈A , Φ = {ϕk }Kk=1 , and Λ = {λa }a∈A .
By integrating out those parameters, we can compute
Equation (1) as follows:
P (D, Z, X|α, β, γ, ρ)∏∏ Γ(ρ + Na0 )Γ(ρ + Na1 )v∈V Γ(N1v + γ)∝Γ(2ρ + Na )
Γ(N1 + γ|V |)
a∈A
∏
∏
K
∏
∏ K Γ(Rak + α)v∈V Γ(Nkv + β)
k=1
×
. (2)
Γ(Nk + β|V |)Γ(Ra + αK)
k=1

a∈A

Here, Na0 and Na1 are the number of words in a’s songs
such that x = 0 and x = 1, respectively, and Na =Na0 + Na1 . The term N1v represents the number of times
that word∑ v was chosen under the condition of x = 1, andN1 = v∈V N1v where∑ V is the set of unique words in D.
Furthermore, Nk = v∈V Nkv where Nkv is the numberof times word v is assigned to topic k under the condition

1400

Perplexityof x = 0. Finally, Rak is the number∑K of times topic k is
assigned to a’s song, and Ra = k=1 Rak .
For the Gibbs sampler, given the current state of all but
one variable zar , the new latent assignment of zar is sampled from the following probability:

LDA
AT

1300

1200

P (zar = k|D, X, Z\ar , α, β, γ, ρ)
Rak\ar + α
Γ(Nk\ar + β|V |)∝Ra − 1 + αK Γ(Nk\ar + Nar + β|V |)∏ Γ(Nkv\ar + Narv + β)×,Γ(Nkv\ar + β)

1100
0

20

30

40

50

Number of topics (K)

(3)

v∈V

where \ar represents the procedure excluding the rth songof a. Moreover, Nar and Narv represent the number ofwords in the rth song of a and the number of times word v
appears in the rth song of a, respectively.
In addition, given the current state of all but one variablexarj , the probability at which xarj = 0 is given by:
P (xarj = 0|D, X\arj , Z, α, β, γ, ρ)ρ + Na0\arj Nzar varj \arj + β∝,
2ρ + Na − 1 Nzar \arj + β|V |

10

(4)

where \arj represents the procedure excluding the jthword in the rth song of a. Similarly, the probability at
which xarj = 1 is computed as follows:
P (xarj = 1|D, X\arj , Z, α, β, γ, ρ)ρ + Na1\arj N1varj \arj + γ∝.
(5)2ρ + Na − 1 N1\arj + γ|V |Finally, we can make the point estimates of the integrated out parameters as follows:
Rak + αNkv + βN1v + γθak =, ϕkv =, ψv =,Ra + αK
Nk + β|V |N1 + γ|V |Na0 + ρNa1 + ρλa0 =, λa1 =.
(6)
Na + 2ρNa + 2ρ
4. EVALUATIONIn this section, we carry out a quantitative evaluation toanswer the following research question: is adopting theartist’s taste for topics effective to model the lyrics generative process?
[Dataset] We used the lyrics of commercially available
popular music. Those lyrics with the song’s title and artist
name were provided by one of the largest companies forcommercial lyrics distribution. We collected data on the
top 1,000 artists in terms of the number of lyrics that areavailable as of the end of December 2016; this gave us
93,716 songs in total. We then extracted Japanese nouns
from each song’s lyrics by using MeCab [20], which is aJapanese morphological analyzer. Nouns that appeared inless than 10 lyrics were eliminated. Although our proposedmodel is language-independent, we used only Japanesewords because of the understandability of the estimatedtopics for Japanese users of Lyric Jumper that we will describe in Section 5. From each of lyrics, we randomly
sampled 80% of the nouns for training data and used theremaining 20 % for test data.
[Settings] In terms of hyperparameters, in line with
150
other topic modeling work, we set α = K
and β = |V|

Figure 2. Perplexity for baseline LDA and proposed AT
model (the lower, the better).
in LDA and the artist’s taste (AT) model. In addition, in
50
the AT model, we set γ = |V
| and ρ = 0.5. To compare the performance of LDA and the AT model, we use
the perplexities of the two models. Perplexity is widelyused to compare the performance of statistical models [1],and the lower value represents the better performance. In
terms of the number of topics, we compute the perplexity
for K = 2, 4, 6, 8, 10, 20, 30, 40, and 50.
[Results] Figure 2 shows the perplexity. As can be seen,
regardless of the number of topics, the AT model outperforms LDA. In both methods, the perplexity reaches a minimum when the number of topics is eight. The differenceof perplexity between the two models becomes larger as
the number of topics increases. From these results, we canconclude that the AT model is superior to LDA for modeling a lyrics generative process and confirmed the effectiveness of modeling a topic distribution for each artist.
5. LYRIC JUMPER
By using the AT model, we implemented a lyrics-based
music exploratory web service called Lyric Jumper thatanyone can use for free without registration. In this section,
we describe the implementation and functions of Lyric
Jumper followed by the log analysis based on the users’
operation logs obtained from the web service.
5.1 Implementation
For Lyric Jumper, the lyrics are provided by the aforementioned company for commercial lyrics distribution. We
used all the lyrics that are available as of the end of December 2016 and extracted Japanese nouns. To guarantee the topic quality, we eliminated artists who had < 10songs and nouns that appeared in < 10 songs. This gave
us 147,990 songs by 3,722 artists.
As for the number of topics K, if K is too small, users
would soon get bored of using Lyric Jumper, while if K
is too large, it would be difficult to understand the difference between topics since many similar topics are generated. Hence, after comparing the topic qualities for several K values, we set K = 20 for Lyric Jumper, although
K = 8 achieved the best result in terms of the perplexity
in Section 4. After automatically estimating the 20 topics
by using the AT model, we manually labeled topic names
so that users can easily understand the characteristics ofeach topic. Examples of topic names are “life,” “sentimental,” and “adolescence.” Although five topics are related
to “love,” our model was able to distinguish between sub-

A 20 topics

B Arst ranking related to “Devoted love” topic

E Recommended arsts

C Top 5 characterisctopics for arst “ClariS”

D Doughnut chart represenng topic tendency of arst “ClariS”

Click “Arst Ranking by Topic” buon

Figure 3. Overview of Lyric Jumper.
tle differences of love: “eternal love,” “devoted love,” “romantic love,” “sexual love (female subject),” and “sexual
love (male subject).”

the topic tendency using a doughnut chart where the circle is divided according to the ratio of the top five topics
D(Figure 3 ⃝).

5.2 Function

5.2.3 Artist Recommendation

Lyric Jumper mainly provides six functions. The following
sections describe the functions one by one.

Since similar artists are one of the important informationneeds in MIR [21], Lyric Jumper provides a similar artists
recommendation function. Lyric Jumper recommends 10
E Among
artists in terms of the topic similarity (Figure 3 ⃝).
the 10 artists, eight artists are popular and two artists areminor. By displaying minor artists as well as popular ones,
Lyric Jumper aims to encourage the user to listen to unfamiliar artists’ songs that are related to his/her favorite artist
by the topic similarity. By clicking a recommended artist’s
graph, the user can jump to the artist’s search result.
Given a selected artist a, we compute the similarity between a and each artist a′ ∈ A \ {a} based on JensenShannon divergence (JSD) between θa and θa′ . The
smaller the JSD value is, the higher the similarity between
artists is. After computing the similarities, we select the
top eight similar artists who have ≥ m songs in the dataset(i.e., popular artists) and the top two artists who have < msongs (i.e., minor artists) and show those 10 artists to theusers. On Lyric Jumper, m is set to 100.

5.2.1 Artist Ranking
Lyric Jumper displays 20 topic names as shown in FigA By clicking one of the 20 topics, Lyric Jumper
ure 3 ⃝.
B
shows up to 100 artists related to the topic (Figure 3 ⃝).
This enables the user to see many artists related to the topic
of his/her interest. The user can also find that unexpectedartists are related to the topic.
Intuitively, given a topic k, artists are ranked based on
both their topic ratio of k (θak ) and the number of songs
assigned to k (Rak ) so that artists more closely related tothe topic are ranked higher. To be more specific, we sort
all the artists in A by using the rank of topic k in θa as thefirst key (the smaller the better) and using the number ofsongs assigned to k (Rak ) as the second key (the larger the
better). Note that artists whose rank of k in θa is lower than
five are not included in the ranking because Lyric Jumper
shows the top five topics for each artist as we will describein Section 5.2.2. Finally, we select the top 100 artists in thesorted list and show them to the users.
5.2.2 Topic Tendency Visualization
When a user clicks an artist, Lyric Jumper visualizes the
topic tendency of the artist. In this function, given artist a,
the top five topics in terms of the occurrence probability in
C The size of a
θa are displayed in rectangles (Figure 3 ⃝).
rectangle corresponds to the topic probability: the larger it
is, the higher the probability is. With this function, a user
can not only understand the topic tendency of the artist’s
lyrics but also find out that the artist sings songs with unexpected topics. We also manually selected four characteristic words for each topic and displayed them below the
topic name so that users can more easily understand themeaning of the topic. In addition, Lyric Jumper visualizes

5.2.4 Phrase Emphasized Lyrics Visualization
When a user clicks a topic of the selected artist, Lyric
Jumper shows the list of song titles of the artist that areassigned to the topic (the song ranking method will be described in Section 5.2.5). When the user clicks a title in
the list, the song’s lyrics are displayed (Figure 4). In the
lyrics, lines3 related to the topic are displayed with emphasis: the stronger the relation is, the larger the font size
becomes and the darker the color becomes. This enables
a user to easily understand the characteristics of the lyrics
such as “the latter half of the lyrics is strongly related tothe topic.” Users can also watch the song’s videos on Lyric
Jumper by clicking the “YouTube Search” button. Lyric
Jumper shows the search results obtained from YouTube4
3 We use the terms “phrase” and “line” interchangeably.
4 https://www.youtube.com/

Lyrics of song
“Surely”
Recommended phrases

A Song ranking related to topic “Roman!c love” for ar!st “ClariS”

Figure 4. Phrase emphasized lyrics visualization.
where the query is the artist name and the song title.
The relevance score between a line in the lyrics and the
topic is computed as follows. We first assign scores forthe nouns in ϕk . Let rank (k, v) be the occurrence probability rank of noun v in ϕk . The score of v is given by
w rel (k, v) = 101 − rank (k, v) if rank (k, v) ≤ 100 andw rel (k, v) = 0 otherwise. Line l consists of n ≥ 0
nouns and can be represented by l = (v1 , · · · , vn ). The
relevancescore of l with topic k is given by l rel (k, l) =∑nw
rel(k, vi ). After computing the scores of all lines
i=1
in a song’s lyrics, the scores are normalized to fit into theinterval [0, 1] by min-max normalization. The font size linearly changes from 16 pt for a score of 0 to 36 pt for a scoreof 1; the color density also linearly changes from #FFFFFF
for a score of 0 to the topic color for a score of 1.
5.2.5 Artist’s Songs Ranking
As mentioned in Section 5.2.4, when a user clicks a topic
of the selected artist, Lyric Jumper returns the ranked list
A Songs are sorted in deof songs in the topic (Figure 4 ⃝).
scending order of relevance to the topic so that the user caneasily access songs that are strongly related to the topic.
The relevance score ∑between song s and topic k is given
by s rel(k, s) = |L1s | l∈Ls l rel(k, l), where Ls is theset of lines in s’s lyrics. That is, we assume that the relatedness between s and k can be represented by the average
relevance between k and each line in s’s lyrics.
5.2.6 Phrase Recommendation
By clicking the “Phrase” button after selecting an artist’s
topic, Lyric Jumper recommends phrases related to the
topic in the artist’s songs (Figure 5). Moreover, every time
the user clicks the “PUSH!” button, a new phrase is recommended. This function enables users to understand there
are various expressions to deliver messages about the topic.
When the user clicks a phrase, Lyric Jumper shows the corresponding lyrics in the same way as in Section 5.2.4.
Given artist a and topic k, the recommended phrases
are selected as follows. In the ith round (i = 1, 2, · · · ), wepool lines that have the ith highest score of l rel(k, l) froma’s songs in order of decreasing s rel(k, s). This round isrepeated until the number of pooled lines is equal to 100.
Lyric Jumper recommends phrases from the pooled list in
random order so that users can see different phrases every
time the user accesses Lyric Jumper.

Figure 5. Phrase recommendation.
Function
PC Smartphone
Artist ranking
2,092
30,295Artist recommendation
1,706
4,016
Artist’s songs ranking
5,399
14,665
253,430
Phrase recommendation 4,997
Table 1. Statistics of use frequency of each function.
5.3 Log Analysis
We released Lyric Jumper as a web service open to the public on 2/21/2017. To analyze users’ exploratory behavior
on Lyric Jumper, we obtained operation logs for 30 days
(2/21 to 3/22). The numbers of unique PC users and smartphone users are 1,288 and 11,065, respectively. The use
frequencies of each function are summarized in Table 1.
We can see that the use frequencies of the artist ranking
and artist’s songs ranking are high. These results indicatethat exploratory search for artists and songs based on topics can stimulate the user’s interest. It can also be observedthat for smartphone users in particular, the phrase recommendation function was used frequently: the push button
was clicked as many as 253,430 times. This data shows
the user’s high information needs regarding finding lyrics
using phrases related to a topic. Compared to these functions, the recommended artists were not clicked very often.
To encourage the artist-similarity-based lyrics exploratory
search, a more sophisticated interface for the recommendation deserves to be explored; we leave this as future work.
6. CONCLUSION
In this paper we proposed a topic model that incorporates
the artist’s taste for topics of lyrics. Our experimental results showed that our model outperformed the state-of-theart LDA model regardless of the number of topics in termsof the perplexity. We also released a lyrics-based music
exploratory web service called Lyric Jumper, where we applied our model to 147,990 lyrics by 3,722 artists. Our
log analysis results show that the phrase recommendation
function, which recommends phrases from the lyrics of theartist’s songs related to the selected topic, achieved a particularly high use frequency.
For future work, since our model is languageindependent, we plan to apply our model to English lyrics
and implement an English version of Lyric Jumper. We
are also interested in combining topics obtained by ourmodel with other features such as audio content and tags.
This would enable users to explore songs by adapting theirsearch intent with increased flexibility.

7. ACKNOWLEDGMENTS
The authors would like to extend their appreciation to
SyncPower Corporation for providing lyrics data. This
work was supported in part by JST ACCEL Grant Number JPMJAC1602, Japan.
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