Catching the attention of a new acquaintance and empathize with her can improve the social skills of a robot. For this reason, we illustrate here the rst step towards a system which can be used by a social robot in order to \break the ice" between a robot and a new acquaintance. After a training phase, the robot acquires a sub-symbolic coding of the main concepts being expressed in tweets about the IAB Tier-1 categories. Then this knowledge is used to catch the new acquaintance interests, which let arouse in her a joyful sentiment. The analysis process is done alongside a general small talk, and once the process is nished, the robot can propose to talk about something that catches the attention of the user, hopefully letting arise in him a mix of feelings which involve surprise and joy, triggering, therefore, an engagement between the user and the social robot.
Engagement is one of the most basic and important phases in interactions between human beings. In the last years there has been a growing interest about this topic throughout the human-machine-interaction (HMI) and related elds [6]. Researchers have highlighted that engagement is a very complex phenomenon, including both cognitive and a ective components: it should involve attention and enjoyment [3] [15].
We refer to this term as the \starting or intention to start an interaction". In particular, we focus our attention on the fact that, in making new acquaintances, the rst impression is very important, and nding as soon as possible common interests to talk about, allows starting an empathetic interaction between two persons, with all that this implies.
In order to trigger both attention and enjoyment, given these premises, it would Copyright ' 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). This volume is published and copyrighted by its editors. SEBD 2020, June 21-24, 2020, Villasimius, Italy. be useful to design a social robotic system which tries to nd the topic of interest of a new acquaintance while attempting to understand what might raise a sentiment of joy attempting to catch an empathetic attention of the user. As a matter of fact, the knowledge of the topics of interest and the \joyful" subjects for the user can lead the rst stages of a conversational interaction that allows the robot to facilitate the engagement of a friendly interaction, instead of a classical trivial interaction between a robot and an human user. To reach this goal, the robot can access the social network data of the new acquaintance trying to coarsely pro le her/his interests, catching useful information to engage a possibly interesting conversation for the user. Social networks represent a great place, maybe the best, to gather information about people's opinions, since they are generally used to express personal thoughts and to discuss with other people about speci c subjects [12][30]. These opinions are really useful to understand and classify the emotion of an event, a product, a person, etc. and analyze his trend [21][22][13].
In this paper we illustrate the design of a system which can be used by a social robot in order to \break the ice" between the robot and a new acquaintance. First of all, the robot acquires a knowledge about the construction of prototypes describing each entry of the IAB Taxonomy.
The system needs a training phase where fundamental concepts, induced by a data driven construction of a conceptual space by using the Latent Semantic Analysis (LSA) procedure and a set of topics derived by the Latent Dirichlet Allocation (LDA) methodology, representing the Tier1 categories of the IAB v2.0 taxonomy are mapped in a semantic space.
A set of tweets is therefore retrieved for each word describing each entry of the IAB Taxonomy. A set of words describing the conceptual axes of two \conceptual spaces" induced from each set of tweets associated to a single IAB entry is built. Each conceptual axis is therefore described by a speci c \bag of words" which constitute the axis description. Each axis is therefore coded as a vector in a semantic space built through LSA, and it is associated to the speci c IAB entry. At the end of the procedure, each entry of the IAB taxonomy is associated to set of vectors, associated to the labels of each fundamental axis of the category, in the built semantic space.
On the other hand, a system which is able to detect a pattern of basic Eckman emotions [24], given a text, is trained too.
Once the system is trained, during a general conversation with a new acquaintance, the robot asks for the user Twitter ID, and while the conversation continues, it retrieves the most recent tweets of the user.
Each tweet is then encoded as a vector in a semantic space. The semantic similarity between each tweet and each vector representing each entry of the IAB taxonomy is computed, and the highest value of similarity is retained. The above procedure allows to associate a tweet of the user to a pattern of IAB categories; furthermore, for each tweet a vector of Eckman fundamental emotions is computed. This leads to a selection of the Tier1 categories of the IAB taxonomy which are of interest of the user and that let arise in the user a speci c emotion. In our case we have chosen to select the "joy" emotion, which is the most desirable when a person meets for the rst time another human being.
The goal is to engage a conversation somehow polarizing it on topics that catch the attention of the user, trying to establish an empathetic relationship. Under some extensions, this approach has relationships with adaptive metaphors, like those developed in other scienti c contexts (e.g., [5]). 2
The proposed system is composed of a set of modules interacting in order to catch the attention of the user. The modularity of the proposed architecture makes it suitable to be implemented on top of Cloud infrastructure (e.g., [10]). The system has a training phase, shown in Fig.1, where a semantic space S is induced from Twitter data and a joyful-topic-detection process, illustrated in Fig. 2, which exploits the Twitter ID of the user in order to retrieve her posts and trying to catch the interests of the user that somehow let arise a \joy" emotion. 2.1
The IAB (Interactive Advertising Bureau ) Tech Lab Content Taxonomy is a concise taxonomy which is also an international standard to map contextual business categories [17] [18]. The latest release of the taxonomy, namely version 2.0, has been released on November 2017 and it accounts 698 entries distributed over 29 Tier-1 classes.
This taxonomy is particularly suited for being used by companies in the market, it is standardized and industry-neutral. These characteristics can be e ectively exploited for pro ling an user interests. 2.2
The dataset object of analysis is retrieved by using the Twitter APIs with the default access level. The default access level gives a random sample of the streaming of publicly available tweets. For our approach, we use only the tweet text content, which is preprocessed before being exploited to build a data-driven conceptual space. Stop-words are ltered out, and links are removed before processing the text since they often hide o -topic posts or even spam. Abnormal sequences of characters were discarded. The retrieval module can be used either for retrieving tweets satisfying a query composed of keywords or to download the last tweets of a given twitter user ID. The Latent Semantic Analysis (LSA) technique is a well-known methodology that is capable of giving a coarse sub-symbolic encoding of word semantics [20] and of simulating several human cognitive phenomena [19]. The LSA procedure is based on a term-document occurrence matrix A, whose generic element represents the number of times a term is present in a document. Let K be the rank of A. The factorization named Singular Value Decomposition (SVD) holds for the matrix A:
A = U
VT
(
AR = UR RVRT
(
R . Furthermore, each j-th dimension can be tagged by considering the words having the highest module values of uij . This makes it possible to interpret the space S as a \conceptual" space, according to the procedure illustrated in [1][25]. 2.4
In the last years a Bayesian probabilistic model of text corpora, namely the Latent Dirichlet Allocation (LDA), has been proposed with the aim of nding topics in documents [2] by associating a set of words to each topic, obtaining a rough representation of a textual corpus.
One on the main advantages of LDA, like LSA, is the fact that the approach
is completely unsupervisedThe only thing required by LDA to setup a priori
is the number N of topics to extract. Latent topics are discovered through the
identi cation of sets of words in the corpus that often occur together within
documents. LDA is based on a generative process according to these two steps:
{ For each topic n = 1; 2; ; N , (n) Dirichlet( ) is a discrete probability
distribution over a xed Vocabulary constituting the n th topic distribution,
and is a hyperparameter for the symmetric Dirichlet distribution.
{ For each document dk of the document corpus, dk Dirichlet( ), which
is a symmetric Dirichlet distribution for the speci c document dk over the
available topics is computed. dk is a low dimensional coding of dk in the topic
space. For each word wi belonging to the dk document, zi Discrete( dj )
and wi Discrete( (zi)) are being computed, where zi is the topic index
for wi
The above process leads to the following distribution
where z; ;
is given by:
p(w; z; ; j ; ) = p( j )p( j )p(zj )p(wj z)
(
This module deals with the detection of emotions in tweets. For the emotional
labeling of tweets, we have considered the six Ekman basic emotions: anger,
disgust, fear, joy, sadness and surprise, exploiting an emotions lexicon obtained
from the Word-Net A ect Lexicon, as described in [26] [27] and adopting a
procedure that has been illustrated in [24], which we brie y recap below.
The methodology is based on LSA and starts from the fact that any text d can
be mapped into a Data Driven \conceptual" space in the sense illustrated above,
by computing a vector d whose i-th component is the number of times the i-th
word of the vocabulary, corresponding to the i-th row of UR, appears in d. This
leads to the mapping of the text as:
dR = dT UR R1
(
The emotional lexicon has been split into six lists, each one associated to
one of the basic Ekman emotions fanger, disgust, fear, joy, sadness, surpriseg.
Fixed an emotion e, a set of 300 arti cial sentences has been built by using
ve randomly selected words belonging to the list related to e. This procedure
has been done for each list associated with a fundamental Ekman emotion,
leading to a set of 1800 arti cial sentences. Furthermore, all the 1542 words
of the lexicon have been considered. Each one of the 3342 (i.e. 1542+1800) b
texts associated with an emotion e has been mapped into the data driven
\conceptual space" induced by TSVD according to the transformation in eq.(
i where b is, time by time, the vector computed starting from one of the 3342 textual artifacts b according to the procedure illustrated at the beginning of this section.
Analogously, any textual content t of a tweet can be mapped into the Data
Driven \conceptual" space by computing a vector t whose i-th component in
the number of times the i-th word of the vocabulary, corresponding to the i-th
row of UR, appears in t. This leads to the mapping of the tweet as:
tR = tT UR R1
(
Once the tweet t is mapped into the \conceptual" space as a vector tR, it is possible to compute its emotional ngerprint by exploiting the vectors bi(e), which act as \beacons" for the vector tR, helping in nding its position inside the conceptual space.
In particular, xed tR, for each set Ee it is computed the weight: we = max cos(tR; bi(e)) (8) once all the six we weights are computed, the vector ft, associated to the vector tR, and by consequence to the tweet t, is calculated as: ft = " pw(Panegewr)e2 ; pwP(feeawr)e2 ; ; wp(sPurperwise2e) # (9) The vector ft nally constitutes the emotional ngerprint of the tweet t in the emotional space. The emotional space is therefore a six-dimensional hypersphere where all tweets can be mapped and grouped. We call the ngerprint ft \emoxel", analogously as the knoxel in the conceptual space paradigm [4]. 2.6
The conversational engine exploits a speech-recognition module which makes use of the Google speech recognition APIs; after that the speech-to-text task is performed, the recognized string is sent to a dialogue manager. A set of questionanswer rules are set-up into the conversation engine in order to start a conversation that leads to the detection of the user interests by transparently invoking the most adequate procedures which analyze the social network posts of the new acquaintance.
The conversational agent engine allows for a natural human-robot interaction. The conversational module is based on a Rivescript engine, which is a a simple scripting language for realizing chatbots and other conversational entities. We have chosen this kind of engine because of the following interesting features: it is plain text, line-based scripting language, simple to learn, quick to type, and easy to read and maintain [23]. The syntax required to build a Rivescript \knowledge base" is very simple: Question-Answers pairs are encoded in plain text; it is easy to write a set of rules that can be combined to build e ective conversational agents; its core library is focused on rendering responses, and it is straightforward to make custom modules and scripts; last but not least is an Open Source tool released under the MIT license [28].
The choice of such an engine allows us to easily connect it to other kind of robots or other kind of services. As a matter of fact, the conversational engine is invoked through a REST service and the answer is delivered to the user after its processing.
A Rivescript knowledge base is made up of Triggers/Replies pairs. Triggers are identi ed by a \+" sign, while Replies are denoted by a \-" sign. For example: + hi - Hello there, my name is SocialRobot,
please could you tell me your twitter ID? the above pair makes it possible that whenever the user says \Hi", the conversational engine replies with \Hello there, my name is SocialRobot, please could you tell me your twitter ID? ".
At the beginning of the conversation, a speci c Rivescript Topic is activated. Topics are logical groupings of triggers. When the conversation is bound in a topic, what the user says can only match triggers that belong to the activated topic [23].
The topic is aimed at entertain a general conversation while the robot peeks the tweets of the user trying to roughly identify the subjects that interest the user and those that speci cally trigger joyful emotions. Once the predominant subject has been identi ed, the robot activates another Rivescript Topic which is of particular interest for the user, trying to establish an empathetic engagement with the new acquaintance.
As an example, let us say that the system nds that, among the di erent higher level categories of the IAB taxonomy, the user is particularly interested in the \Automobiles" topic and that some of his tweets show the \joy" emotion for that topic, the conversation will be switched to the \Automobile" Topic and speci c sentences will be said by the robot in order to catch the user attention and empathy, like \Great! with my superpowers I can see that you like automobiles. I like the brand automobiles! Which one do you prefer?" 3
We have presented a preliminary work on a system that tries to catch the attention of a new acquaintance with the aim of establishing a rst engagement with the user.
The system uses both LSA and LDA descriptors, as well as an emotion detection module to reach this goal. A conversational engine guides the initial process and continues witht the conversation.
Many issues have to be enhanced, starting from a more ne grained classi
cation which should be also fast and reliable, the selection of speci c entities that
can catch in a more e ective manner the attention of the user, as well as the
automatic generation of conversational statements starting from the user tweets.
Other lines of research shall consider privacy-preservation issues (e.g., [8, 11]),
as well as complex web intelligent solutions (e.g., [7, 9]).
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