Building a Conversational Model from Two-Tweets
Ryuichiro Higashinaka1, Noriaki Kawamae2, Kugatsu Sadamitsu1, Yasuhiro Minami3
Toyomi Meguro3, Kohji Dohsaka3, and Hirohito Inagaki1
1

NTT Cyber Space Laboratories, NTT Corporation
1-1 Hikarinooka, Yokosuka, Kanagawa, 239-0847 Japan{higashinaka.ryuichiro,sadamitsu.kugatsu,inagaki.hirohito}@lab.ntt.co.jp
2

NTT Comware Corporation
1-6 Nakase, Mihama-ku, Chiba 261-0023 Japan
kawamae.noriaki@nttcom.co.jp
3

NTT Communication Science Laboratories, NTT Corporation
2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237 Japan{minami.yasuhiro,meguro.toyomi,dohsaka.kohji}@lab.ntt.co.jp
Abstract—The current problem in building a conversationalmodel from Twitter data is the scarcity of long conversations.
According to our statistics, more than 90% of conversationsin Twitter are composed of just two tweets. Previous work has
utilized only conversations lasting longer than three tweets for
dialogue modeling so that more than a single interaction canbe successfully modeled. This paper veriﬁes, by experiment, that
two-tweet exchanges alone can lead to conversational models thatare comparable to those made from longer-tweet conversations.
This ﬁnding leverages the value of Twitter as a dialogue corpus
and opens the possibility of better conversational modeling using
Twitter data.

I. I NTRODUCTION
Twitter offers useful data for analyzing social communication, such as how information spreads among people [1], [2].
Twitter data can also be useful for mining timely opinions on
the web for marketing purposes [3]. Recently, because of theconversational nature of Twitter (e.g., mentions and replies)
and because of its vast data size and diversity of content compared to what the dialogue research community has seen, there
has been emerging work on creating stochastic conversationalmodels, such as hidden Markov models (HMMs), from Twitter
data [4]. Building such models can be useful for analyzing
how humans exchange utterances and obtaining insight into
building automated text/spoken dialogue systems.
Although it is true that Twitter data are conversational andlarge in size, there is a severe limitation when we want tobuild a conversational model from Twitter data. That is, only
a very small proportion of all posts form conversations, and,what is worse, most of such conversations consist of only two
tweets (a post and a reply; hereafter referred to as two-tweets).
According to a previous study, 37% of English tweets arereported to be conversational [5], of which 69% are two-tweets[4]. This tendency is the same in Japanese. According to ourstudy (see Section IV-A), only about 5.1% of all Japanese
tweets form conversations, of which 91% are two-tweets. Notethat we call two or more tweets connected with an in-replyto relationship a conversation, which is different from Kelly’s
idea. He regards all tweets starting from @ (an indicator of978-1-4673-0367-5/11/$26.00 ©2011 IEEE330

addressing other users) conversational, which we consider to
be too lenient since there may not be a reply to an address.
Typically, when we want to build a conversational model,
we need conversations longer than just two tweets, because
conversations in general are not simply about a single interaction, such as a question and answer. Minimally, we needconversations of three or more tweets (hereafter referred to as
long-tweets) to build reasonable conversational models when
we want to deal with more-than-one-shot interactions. This
is why previous studies have used conversations longer than
three tweets to train their models [4]. Now the problem is that
we have a very small number of long-tweets, which severely
limits the size of training data for conversational modeling.
This paper proposes to make use of two-tweets to build aconversational model that can be equivalent to or better than
one built from long-tweets. Our basic idea is to cluster tweets
within two-tweets to form pseudo long conversations, fromwhich we can train a conversational model. In our approach,
we use a non-parametric Bayesian method called the inﬁnite
HMM for the modeling. Our approach could make full use of
the conversations in Twitter and has the potential to leverage
the performance of conversational models that can be learned
from Twitter data.
II. R ELATED W ORK
Modeling conversations from dialogue data has long beenstudied, mainly by using HMMs for their usefulness in dealing
with sequential data. Shirai trained ergodic HMMs from thedata of task-oriented spoken dialogues to analyze the functionsof audio-visual information in dialogue [6]. Dialogues withless task restriction have also been modeled using HMMs;
Isomura et al. used HMMs to model interview-like conversations and used the HMMs to evaluate the naturalness of
conversations [7], and Meguro et al. used HMMs to analyze
counseling-like listening-oriented dialogues for the purposeof building listening agents [8]. Engelbrecht et al., and Higashinaka et al., both trained HMMs that can predict turnwise user satisfaction transitions within a dialogue [9], [10].
All these studies use long conversations that at least last

ASRU 2011

three turns; typically one conversation lasts more than a few
minutes, resulting in tens of turns per dialogue. The problem
in these studies is that collecting conversations using humansubjects is usually very costly, resulting in a limited numberof dialogues with restrictions in dialogue topics.
Twitter data could overcome such a problem by its data
size and diversity in content, but as we mentioned in the
introduction, modeling Twitter conversations needs to deal
with the scarcity of long conversations. Currently, only longtweets are used for training conversational models as in [4],which we consider does not make the best use of conversationsin Twitter. To the best of our knowledge, no work has tackled
the problem of the scarcity of long conversations in Twitter.
In the context of modeling Twitter data, Ramage et al., used
a technique called labeled latent Dirichlet allocation (labeled
LDA) to model a large number of tweets in order to understandthe varied content in Twitter [11], but their focus is to cluster
individual tweets by latent topics, not to model conversations.
Joty et al. proposed to model conversations using an HMM(called HMM+Mix) [12]; however, they use the data of email discussions and messages at Internet forums, where theconversations are typically longer than those found in Twitter,and therefore do not address the lack of long conversations.

tweets. Since we have more than a million two-tweets in ourdata set (see Section IV-A), a straightforward application of
Gibbs sampling would be extremely difﬁcult. Therefore, in
our approach, we insert one step before HMM training; thatis, the creation of small subsets. To make such subsets, wetake a grep approach; we grep the entire tweets by keywords
to create keyword-related subsets of tweets. Compared toapplying sophisticated hard-clustering algorithms for creating
subsets of data, we ﬁnd this approach promising and attractive
because we can ascertain that a subset is concerned witha certain topic semantically constrained by keywords. One
difﬁculty of this approach is that tweets are generally short,and this simple grep approach may end in low recall. To
overcome this problem, following [17], we turn to Wikipedia
to annotate tweets with Wikipedia concepts (titles/entries) sothat we can additionally include such concepts as our grep
target and thereby leverage the coverage of tweet extraction.
We ﬁrst describe how we use Wikipedia to annotate tweets
with Wikipedia concepts in order to create subsets. Then, we
describe how we train our model using the inﬁnite HMM.

A. Making a Subset of Tweets using Wikipedia

III. A PPROACH
We aim to use two-tweets to create conversational models
that have only been realized by using long-tweets. We consider
that, by using clustering techniques, we can cluster tweets
within two-tweets to form pseudo long conversations, fromwhich we can train a conversational model for long conversations; that is, we ﬁrst ﬁnd two-tweets, such as A → B andB  → C, where B and B  are similar tweets, and cluster thesimilar ones to form a pseudo three-tweet conversation (i.e., A→ {B, B  } → C). If we can apply this process to many twotweets and obtain the optimal clusters with their transitions,then that structure would make a model for long conversations.
Since this process is the same as what HMM training does,
we can adopt known algorithms of HMMs for this task. In
this paper, we employ a non-parametric Bayesian version of
the HMM; namely, the inﬁnite HMM, and use Gibbs samplingfor parameter estimation. The inﬁnite HMM is related to the
Dirichlet process [13], which is a non-parametric Bayesianmodel [14]. The hierarchical Dirichlet processes (HDP) [15]is one realization of the Dirichlet process for handling mixture
models, and the inﬁnite HMM is one implementation of
the HDP. The inﬁnite HMM has been applied to modelingsequential data when the number of states is not known inadvance; the optimal number of states is determined by data.
We ﬁnd this feature useful because Twitter data are diverse incontent and it is difﬁcult to estimate the number of states inadvance. In addition, a Bayesian approach has been reported
to perform better [4] than the EM algorithm [16].
One possible drawback of the above approach is that Gibbs
sampling is computationally heavy. Even when we are to use
only two-tweets in Twitter, which constitute a small proportionof all tweets, we still need to handle a large number of331

Banerjee et al. annotated tweets with Wikipedia concepts[17]. They ﬁrst made a text database of Wikipedia articles.
Then, for each tweet, they queried the database using thewords in the tweet to retrieve top-N Wikipedia articles (theyused 20 for N). They used the titles of the retrieved articles
to semantically augment tweets for better clustering.
Our approach is similar to theirs in that we use Wikipedia tocomplement the information of tweets, but different in that wetake a more direct approach. We ﬁrst add all Wikipedia titles
to the dictionary of a morphological analyzer so that words
that match Wikipedia titles can be detected in morphologicalanalysis. Second, we create a database of Wikipedia titles andtheir categories (NB. Wikipedia usually has several categories
associated with each title). Since Wikipedia categories have ahierarchical structure, for each title, we also include categories
that are one layer above the categories directly associated withthat title. Such hypernym categories can be useful for adding
generalized meanings to the titles. For example, the word “wcup (daburyuu hai)” is associated with a category “world cup(wârudo kappu)” together with their upper layer categories“world championships (sekai senshuken)” and “international
sports competitions (kokusai supôtsu kyougi taikai)”. Third,
we process all tweets by the morphological analyzer and detect
Wikipedia titles. The detected titles are then coupled with theircategories using the database. Finally, given such processed
tweets and keywords KW , we can create a subset of the tweets
by ﬁnding those that contain KW . Note that grep extracts
tweets that have KW in the tweets as well as in the associatedWikipedia categories. In this approach, for example, a tweet
having “w-cup” can be extracted by keywords such as “worldcup” as well as “sports” and “championships”.

TABLE IS TATISTICS OF OUR T WITTER CORPUS .

B. Modeling Tweets by the Inﬁnite HMM
In the inﬁnite HMM, tweets are processed one by one.
The ﬁrst tweet is clustered to the initial cluster (NB. there
is only one cluster at the beginning). Then, the next tweet ti
is clustered to an already occupied cluster cj or creates a newcluster (cj=new ) with the probabilityP(cj |ti ) ∝ P(cj |cti−1 ) · P(cti+1 |cj ) · P(ti |cj ),where ct means the cluster of a tweet t. In a conversation,
tweets are given a sequential order; ti−1 and ti+1 denotethe previous and next tweets of ti . P(ck |cj ) is a transition
probability:transitions(cj , ck ) + β

P(ck |cj ) = K

l=1 transitions(cj , cl ) + K · β + α,where α is the hyper-parameter that determines how likely a
new cluster is created, K is the number of occupied clusters,
and transitions(cj , ck ) returns the number of transitions fromcj to ck . β is a ﬂooring value to avoid zero probability. P(ti |cj )is the probability that ti is generated from cj ; that is,P(ti |cj ) =P(w|cj )count(ti ,w) ,w∈Wcount(cj , w) + γ,w∈W count(cj , w) + |W | · γP(w|cj ) = 

where W is a set of features (e.g., bag-of-words), count(∗, w)
a function that returns the number of occurrences of a feature
w for a tweet or a cluster, and γ the hyper-parameter.
The probability of creating a new cluster isP(cnew |cti−1 ) · P(cti+1 |cnew ) · P(ti |cnew ),where P(cnew |cti−1 ) and P(cti+1 |cnew ) are derived byP(cnew |cti−1 ) = Kαl=1 transitions(cti−1 , cl ) + α

P(cti+1 |cnew ) =

,

1
.
K+1

Here, we use a uniform distribution for P(ti |cnew ).
After all tweets have been processed, Gibbs sampling is
performed; that is, we repeatedly select one of the tweets
from its cluster and relocate that tweet as if it were the last
tweet to be clustered. After performing a sufﬁcient number ofsamplings, we obtain the optimal number of clusters together
with their emission probabilities and transition probabilities,
which become our conversational model.
Determining the appropriate features for representing tweets
is a difﬁcult problem. Following previous studies [4], [12],we use bag-of-word-unigrams in this paper. This choice offeatures is also backed by the fact that there are too many
unique words in Twitter data (see Section IV-A), suggestingthat features made using bigrams or longer n-grams would be
too sparse.

332# conversations# tweets# words# unique words

food
63312
132203251717974865

sports
37292
78123
187038275309

all1211725
2500918
40098705
452099IV. E XPERIMENT
To verify our approach, we performed an experiment. We
ﬁrst collected tweets from the public timeline. Then, we madetwo subsets of tweets related to food and sports, which we
consider to be common topics in everyday conversation. Since
our aim is to examine whether the two-tweets can be used tocreate models that can be achieved by long-tweets, we further
divided the subsets into two-tweets and long-tweets. Finally,
we examined whether conversational models made from twotweets can compete with those made from long-tweets.
A. Data Collection
We collected Japanese tweets from the public timeline using
Rest and Streaming APIs between February 2 and September
15, 2010. We used the default access level called “Spritzer”.
In this period, we crawled over 95 million tweets (95,501,894
tweets). From them, we retrieved conversations using the inreply-to ﬁeld of the tweets (4,907,519 tweets; 5.1%). Afterdiscarding conversations starting with replies, the resulting
corpus of conversations contains about 2.5 million tweets. Thisis about 2.62% of the crawled data, which shows just how
scarce conversations are.
Having created the corpus, we ran our morphological analyzer JTAG [18] with a user deﬁned dictionary augmented
with Wikipedia titles (see Section III-A). We used the Japanese
Wikipedia dump of April 20, 2011. We also created a databaseof titles and their categories from the same dump. After all
tweets had been annotated with Wikipedia categories, wecreated two subsets by grep using “meal|food (shokuji|ryouri)”(‘|’ indicates OR) and “sports (supôtsu)” as keywords. We callthe subsets here Food-Set and Sports-Set. Here, grep was doneon the conversation level; that is, when one of the tweets in
a conversation had a keyword, that entire conversation was
extracted.
Table I shows the statistics of our Twitter corpus together
with the information of the two subsets. We can conﬁrm thediversity of content in Twitter from its large vocabulary size.
From the fair size of the subsets, we can also conﬁrm theeffectiveness of our utilizing Wikipedia. Table II shows thenumber of N-tweet conversations. We can see that most are
two-tweets. We can also see that, as N increases, the numberof tweets decreases exponentially. This shows the difﬁculty ofusing Twitter data as a dialogue corpus and makes clear theneed to make use of two-tweets, which account for over 90%
of all conversations (2,280,402 tweets; 91% of all tweets in
our conversations).
B. Evaluation Procedure
We divided each subset into two sets. One set comprises
only two-tweets (the 2-tweet set) and the other set long-tweets

TABLE II
N UMBER OF N - TWEET CONVERSATIONS .
N
2
3
4
5
6
7
8
3≤

food
58269 (92.03%)4565 (7.21%)426 (0.67%)
46 (0.07%)
6 (0.01%)0 (0.00%)0 (0.00%)
5043 (7.97%)

sports34114 (91.48%)
2865 (7.68%)
273 (0.73%)
34 (0.09%)
5 (0.01%)0 (0.00%)
1 (0.00%)
3178 (8.52%)

TABLE IIIT HE NEGATIVE LOG LIKELIHOOD (LL) AND K ENDALL’ S TAU FOR THE
LONG - TWEET TEST SET BY THE 2- TWEET MODEL , THE LONG - TWEETOPEN MODEL , AND THE LONG - TWEET CLOSED MODEL . * AND + INDICATESTATISTICAL SIGNIFICANCE ( P < 0.01) OVER THE 2- TWEET AND
LONG - TWEET OPEN MODELS , RESPECTIVELY.

all
1140201 (94.10%)66223 (5.47%)
4729 (0.39%)506 (0.04%)
62 (0.01%)
3 (0.00%)
1 (0.00%)
71524 (5.90%)

Food-Set LL
Food-Set tau
Sports-Set LL
Sports-Set tau(the long-tweet set). We further divided the long-tweet set into
two sets by dividing it in half; we call them the long-tweet
train set and the long-tweet test set. In Food-Set, the longtweet train set and long-tweet test set have 2522 and 2521
conversations, respectively. In Sports-Set, the long-tweet train
set and long-tweet test set both have 1589 conversations.
Since we are interested in how the conversational model
made from two-tweets competes with that made from longtweets, we trained models from the 2-tweet set and the longtweet train set and evaluated them by looking at how they
explain unseen long conversations; that is, the long-tweet test
set. We also used the long-tweet test set to train a model andevaluate it using itself as test data (i.e., closed test), which
will indicate the upper bound. We are also interested in how
a model improves when we increase the training data. To
investigate this, we split the 2-tweet set into blocks of 1000
conversations each, and investigated how a model improves
by adding blocks to the training data.
C. Evaluation Metrics
For evaluation metrics, we used log likelihood (LL) andKendall’s tau, which have been used in a previous study [4].
For LL, we used the forward algorithm for calculation [16].
For Kendall’s tau, we ﬁrst created all possible orders of tweets
in each conversation in the long-tweet test set, and calculated
the LL of each order and selected the order with the highest
LL. Then, that order was compared against the original orderto calculate Kendall’s tau by:tau(R, H) =n+ (R, H) − n− (R, H),
combination(R)where R and H denote reference and hypothesis orders,n+ (R, H) the number of correct pairwise orders, n− (R, H)
the number of incorrect pairwise orders, and combination(R)the number of possible pairwise orders. The high value in
Kendall’s tau suggests that the ﬂow of conversation has beensuccessfully modeled.
D. Training inﬁnite HMMs
We trained our inﬁnite HMMs from (a) the 2-tweet set, (b)the long-tweet train set, and (c) the long-tweet test set of FoodSet and Sports-Set. We call the models trained from (a), (b),and (c) the 2-tweet model, the long-tweet open model, and thelong-tweet closed model, respectively. We used tentative valuesof 0.01 for all α, β, and γ in HMM training (cf. Section III-B).
For features, we used bag-of-word-unigram features, where thewords are the top-5000 words in the 2-tweet set. The number

333

2-tweet290.70+0.312+
332.360.308+

long-tweet open
294.710.247
331.92∗0.170

long-tweet closed
285.54∗+0.277
320.86∗+0.303+of top-N words follows the convention [4], [12]. The numberof iterations for Gibbs sampling was set to 1000, which meansthat each tweet is considered 1000 times for relocation.
E. Results
Table III shows the negative log likelihood (the lower, thebetter) and Kendall’s tau averaged over all test conversations(i.e., the long-tweet test set) for the obtained models. We
performed the Wilcoxon rank-sum test to check whether themodels are signiﬁcantly different. As a result, we found thatthe 2-tweet models signiﬁcantly outperform the long-tweet
open models in almost all cases and that their performance caneven attain the level of the long-tweet closed models in somecases; for example, there is no statistical difference between
the 2-tweet model and the long-tweet closed model for tau inboth Food-Set and Sports-Set. This indicates that it is possible
to use two-tweets to model long conversations.
Figures 1 and 2 show the line plots of the negative LL and
Kendall’s tau against the long-tweet test set depending on thesize of the 2-tweet set of Food-Set and Sports-Set, respectively.
The solid blue and red lines indicate the possible upper bounds(i.e., results of the long-tweet closed models) of the LL andtau, respectively. The dotted blue and red lines indicate theperformance of the long-tweet open models. As for the LL,
as we increase the training data, the performance gradually
passed or closely neared the dotted line. Since the performancereaches that of the long-tweet open models when the numberof training conversations is 5000-10000, this number of twotweets is what we need to compete against 1500-2500 (i.e.,the size of our long-tweet train sets) long-tweets. It is also
noticeable that the LL somewhat degrades after 20000 twotweets, probably because of some structural changes in the
HMMs to better cope with longer tweets, which instead led to
improvements in tau. The result for Kendall’s tau seems better
than that for the LL; the 2-tweet models steadily reached theperformance of the long-tweet closed models. This indicates
that, in terms of ordering, two-tweets seem very helpful for
creating better conversational models.
Figure 3 shows the number of states of our 2-tweet models
depending on the size of training data. Remember that the
optimal number of states is automatically decided from datain the inﬁnite HMM. It can be seen that we require about 3540 states for the food and sports topics. This number is close tothat reported in [4] when their models’ performance saturated.
Although we need further veriﬁcation by using different hyperparameters, 35-40 could be the rough estimate of utterance

50

tau (long-tweet open)
LL (2-tweet)tau (2-tweet)

45

0.35

300

0.3

0.25

290

40

Number of states

310

Kendall’s tau

Negative log likelihood

LL (long-tweet closed)
LL (long-tweet open)
tau (long-tweet closed)

30
25
20
15

5
0

0

10000

20000

30000

40000

50000

0.15
60000

Training data size (number of conversations)

LL (long-tweet closed)
LL (long-tweet open)
tau (long-tweet closed)

Fig. 3.

tau (long-tweet open)
LL (2-tweet)tau (2-tweet)

350

0.4

0.35

330

0.25

0.2

320

Kendall’s tau

340
0.3

0.15
310
0

5000

10000

15000

20000

25000

30000

0

10000

20000

30000

40000

50000

60000

Training data size (number of conversations)Fig. 1. Learning curve: the negative log likelihood (LL) and Kendall’s tau
against the long-tweet test set depending on the size of training data (2-tweets)
for Food-Set. The red solid and dotted lines indicate the LL for the long-tweet
closed and open models, respectively. The blue solid and dotted lines indicate
Kendall’s tau for the long-tweet closed and open models, respectively.

Negative log likelihood

Food-Set (long-tweet closed)
Food-Set (long-tweet open)
Sports-Set (long-tweet closed)
Sports-Set (long-tweet open)
Food-Set (2-tweet)
Sports-Set (2-tweet)

10

0.2

280

35

0.1
35000

Training data size (number of conversations)Fig. 2. Learning curve: the negative log likelihood (LL) and Kendall’s tau
against the long-tweet test set depending on the size of training data (2-tweets)
for Sports-Set. See Fig. 1 for the descriptions of the red and blue solid anddotted lines.

variations in Twitter.
F. Analysis of Obtained HMMs
To understand our obtained HMMs, we looked at how
our 2-tweet models decode long-tweets. For this purpose,
we obtained best paths for the long-tweet test set in the 2tweet model using the Viterbi algorithm. Then, we visualized
frequent paths in the best paths. Figure 4 shows the resulting
graph. Here, only the paths that occurred more than 15 times
are shown. States that do not have such frequent paths andthe paths to the END state have been removed for better
visibility. The numerical numbers along the edges indicate theprobabilities of choosing those paths. The graph clearly showsthe existence of long sequences of tweets in the trained modeleven though the model is trained from two-tweets, conﬁrming
the feasibility of using two-tweets to model long conversations.
To further analyze the graph, we examined what words are334

The number of optimal states depending on the training size.

representative in the states. Table IV shows the representative
words for some of the states in Fig. 4. To select the representative words, we used the log-likelihood ratio test, which issimilar to the chi-square test; that is, for a state in question,
we count the occurrences of a word in the tweets clustered
to that state (see Section III-B) and tested whether that countis signiﬁcantly higher than its expected value. Since the loglikelihood ratio follows the chi-square distribution, we selected
representative words that had the log-likelihood ratio of over
15.13 (p<0.0001).
To make it easier to grasp the underlying meaning of therepresentative words in the table, we included our interpretations of them in square brackets. For example, in state 29,
we see words that concern the status of people; e.g., comehome, wake up, work, and sleepy. State 11 also concerns
one’s status, but is more oriented towards activities at home.
In state 13, we have interrogatives. In state 6, 18, and 31, we
have some response variations; namely social, affection, andemotion. In state 26, we see words that report one’s meals,
and in state 27, we see the description of food, such as foodnames and ingredients. States 26 and 27 seem to be somewhat
topic-dependent states. State 7 seems to represent a genericcomment; people expressing their opinions/attitudes in reply
to previous tweets. Because many states come in to this stateand few edges go out, this state seems to be the terminal of
conversation in Twitter.
Below, we list some sequences from the graph. The sequences seem reasonable and bears some similarity to thetypical conversation ﬂow in Twitter [4], again showing thefeasibility of using two-tweets for conversational modeling.
• 11:status (home) → 6:response (social) → 7:comment• 29:status → 31:response (emotion) → 18:response (affection) → 7:comment• 26:report → 13:question → 27:description → 7:comment
V. S UMMARY AND F UTURE W ORK
In this paper, we ﬁrst pointed out that Twitter data scarcely
contain long conversations and that most of the conversations(91% in Japanese tweets) are composed of two tweets.

START

0.057

0.024

26

0.009

11

25

0.556

0.092

0.121

0.412

31

0.077

0.392

0.014

0.017

0.108

7

0.030

0.016

0.457

13

0.073

8

0.250

0.351

0.064

30

0.014

0.282

Fig. 4.

7
11
13
18
26
27
29
31

0.042

0.016

0.030

0.091

0.055

0.606

0.164

36

0.139

0.541

0.164

0.048

0.356

0.249

0.070

19

0.209

0.040

4

0.082

0.044

0.470

22

0.230

0.164

18

0.223

0.147

Graphical representation of transitions between states for the food topic.

TABLE IV
R EPRESENTATIVE WORDS OF EACH STATE FOR THE FOOD TOPIC . ID
MEANS THE STATE ID. T HE SQUARE BRACKETS INDICATE OURINTERPRETATIONS OF THE REPRESENTATIVE WORDS . E NGLISH WORDS
ARE TRANSLATIONS BY THE AUTHORS AND THE ORIGINAL JAPANESE
WORDS ARE GIVEN IN PARENTHESES .
ID
6

0.033

0.040

0.091

27

0.029

0.089

0.254

0.127

0.014

0.091

0.203

34

0.370

0.190

0.109

0.329

0.025

0.115

0.115

3

0.065

0.310

6

0.487

0.058

0.025

29

0.045

we may be able to obtain better emission probabilities. We
also wish to build an automated dialogue system based on theobtained conversational models.
R EFERENCES[Interpretation] Representative words[response (social)] welcome home (kaeri, kae, nasai, nasa),
you must be tired (otsukaresama), good night (oyasumi)[comment] ﬁrst person pronoun (watashi), sentence-ending
particles that express conﬁrmation or agreement (ne, yo, yone,desune), think (omou), lol (warai)[status (home)] come home (kitaku), I’m home (tadaima),!, meal (gohan), now (nau, ima), from now (korekara), bath
(furo), dinner (yuuhan), ﬁnished (shuuryou)[question] what (nani), okay (daijoubu), ?, where (doko),what kind of (donnna), how (dou)[response (affection)] please (kudasai), !, thank you (arigatou), congratulations (omedetou), good luck (ganbaru), I hopeto (yoroshiku), take care (daiji)[report] today (kyou), lunch (ohiru), tonight (konya), yummy(umauma), ramen noodles (râmen), hamburger (hanbâgu),
curry (karê), set meal (teishoku)[description] salad (sarada), tomato (tomato), fried (itame),miso soup (miso shiru), soy sause (shouyu), vegetables (yasai), pork (butaniku), onion (tamanegi)[status] meal (gohan), come home (kitaku), sleep (neru),
wake up (okiru), work (shigoto), sleepy (nemui)[response (emotion)] ), (, *, ˆ, ·, ‘, ∀, o, ;, -, , ’, ≤, ≥, !,(characters used for facial expressions)

Previous work has utilized only conversations lasting longer
than three tweets for dialogue modeling so that more than asingle interaction can be successfully modeled. This paper has
veriﬁed by experiments that two-tweets alone can also lead togood conversational models that are comparable to those madefrom long-tweets. This ﬁnding leverages the value of Twitteras a dialogue corpus and points the way to making better useof conversations in Twitter for conversational modeling. As
future work, we want to consider the possibility of using single
tweets to enhance our models. Since our approach is analogous
to estimating trigrams from bigrams, the same can be said for
unigrams. Although single tweets do not affect the transitions,335[1] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, “Everyone’s
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