=Paper=
{{Paper
|id=Vol-2421/IroSvA_paper_8
|storemode=property
|title=UFPelRules to Irony Detection in Spanish Variants
|pdfUrl=https://ceur-ws.org/Vol-2421/IroSvA_paper_8.pdf
|volume=Vol-2421
|authors=Diulia Justin Deon,Larissa Astrogildo de Freitas
|dblpUrl=https://dblp.org/rec/conf/sepln/DeonF19
}}
==UFPelRules to Irony Detection in Spanish Variants==
https://ceur-ws.org/Vol-2421/IroSvA_paper_8.pdf
UFPelRules to irony detection in Spanish
variants
Diulia Justin Deon1 and Larissa Astrogildo de Freitas1
UFPel, Gomes Carneiro 1, Porto, 96010-610, Rio Grande do Sul, Pelotas, Brazil
djdeon,larissa@inf.ufpel.edu.br
Abstract. Figurative language is one of the most difficult challenge to
Natural Language Processing. In this study, we propose a strategy to
irony detection in Spanish based in linguistic patterns. After studying
the state of the art, we implement seven linguistic patterns in 9000 texts
written in three different linguistic variants.
Keywords: Irony detection · Spanish variants · Patterns
1 Introduction
Usually, irony is known as a way of communicating the opposite of the literal
meaning [3]. Commonly known as, irony is a figure of speech that seeks to express
a word or text with distinct meaning from the original.
The detection of ironic declarations represent a major challenge to Natural
Language Processing. In this study, we analyzed and implemented some linguistic
patterns that may be associated with ironic declarations in Spanish language
varieties.
Irony can be seen as a complex communication mechanism that is governed
by pragmatic principles. Observing the uses of the term, it is observed that irony
is often mistaken for sarcasm, satire or parody. In this study, we based the task
of irony detection in a general concept for this phenomenon, since there are no
consensuses on a rigid definition of irony. [8] and [4] define irony as an apparent
violation of pragmatic principles in an utterance. [1], on the other side, define
irony as a contradictory property in a given context or event. [7] states that the
presence of irony conveys a pragmatic meaning when alluding to expectations
(failures or not).
The elaboration of patterns involving possible evidence of ironic declarations
considers the following elements: syntactic rules, static expressions, lists of laugh-
ter expressions, specific scores, and symbolic language. The implementation of
the proposed patterns is based on the work of [6], and [3].
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). IberLEF 2019, 24 Septem-
ber 2019, Bilbao, Spain.
Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019)
Six linguistic patterns classified into three groups were implemented: (C1)
lists; (C2) exact expressions; and (C3) symbols (Table 1). Finally, we applied
these patterns in 9000 texts and analysed the results.
2 Related Works
The detection of irony has been increasingly worked and explored by the scientific
community.
The use of linguistic patterns to detect irony has already been used by other
researchers, such as [3] and [6].
[3] proposed 13 linguistic patterns to detect irony in a corpus of tweets in
Brazilian Portuguese about End of the World.
[6] proposed an investigation from a corpus of comments. In this study, the
authors concluded that the most productive patterns for the detection of irony
are related to the use of punctuation marks, emoticons and quotes.
3 Linguistic Patterns
List of patterns were built in the three varieties of the Spanish language that
are present in the corpus: Mexican, Spanish and Cuban [5], [2]. In this study six
linguistic patterns were implemented for all varieties. They are:
Table 1. List of patterns.
Categories Patterns Expression
C1 P1 List of Laughter Expression
C2 P2 “no te creas”,“no es cierto”, “es broma”
P3 ...!
P4 Uppercase
C3
P5 * | * | !* | ?* | ** | ** | !*?* | ?*!*
P6 Quotations Marks
P1- List of Laughter Expression: Social network’s users, like twitters,
usually use expressions like “jajaja” and “hehehe” to express that they are kid-
ding or to reverse the utterance’s meaning. For example, “Jajajajajajajajaja
tendencioso, a mi primero que me explique la falta de capacidad de sus can-
didatos y luego hablamos de conflicto de intereses.”.
P2 - “No te creas”, “No es cierto”, “Es broma”: In Brazilian Por-
tuguese, people use the expression “Só que não” to add a negative meaning to
all that said before. In Spanish, we found three equivalent expressions: “No te
creas”, “Es broma” and “No es cierto”. For example,“Pues no te creas que es
tan jovencita.”.
P3 - ...! : Expressions accompanied by an exclamation point after
an ellipsis are considered ironies, because the punctuation (...) indicates that the
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phrase is over. However, when such a pattern is used, it would be interpreted
as an attempt by the author to add something, finish his sentence, which is
supposed to be an irony. For example, “Ojalá Carmen Calvo saliendo a decir
‘Pero no os habéis dado cuenta? Todo este tiempo... el relator... eras tú!’ y se va
volando agarrada a un paraguas.”.
P4 - Uppercase: To give greater intonation and prominence to the texts,
users choose to use words with uppercase formatting in text points, or all of it,
indicating that his speech would change, producing the feeling that this is an
irony. For example: “LA TV DIGITAL ESTA SIENDO MUY EFICIENTE.”.
P5 - * | * | !* | ?* | ** | ** | !*?* | ?*!* : People end up not using more
final scores in sentences, and in the case of Twitter, they use even less because
the network counts and only allows tweets with a certain number of characters.
Analyzing this, it is noted that the score is no longer used in the application in
question. Thus, when it is present in some phrase in a large number followed,
called repetition, it brings indicators that the phrase contains irony. For example:
“entonces la tv cubana PAGA los derechos? !VIVA EL pqt!!!!!!!!!!!!!!!!!!!!!!”.
P6 - Quotations Marks: Quotes are used to give prominence, show that
it would be something similar or derived from, causing the reader a sense of
insecurity referring to what is being read, and thus indicating, that it may be
an irony, a phrase using figured sense. For example: “Entendi ‘perfectamente’ ,
ms claro no pudieron ser. Que cosa es servicio de mensajeria ??”.
4 Training set
In this work, we used the training set, available in IroSvA. This database contains
7200 texts with the following annotation: identification code (ID), topic (text
subject), classification of ironic or non-ironic.
UFPelRules classified 964 Cuba texts, 864 Mexico texts and 749 Spain texts
as ironic (Table 2a, 2b and 2c) on the training set.
There were 2400 texts for each spanish variation, of these 800 were ironic.
UFPelRules was able to identify 964 text with irony in the Cuban variety (Table
2a), 1463 in the Mexican variety (Table 2b) and 743 in the Spanish variety
(Table 2c). Comparing the results of UFPelRules and the training set, the tool
identified and marked more texts as ironic in Table 2a and 2b, and less in Table
2c, in relation to those predefined by the training set.
These results demonstrate that the tool can detect patterns, with half of the
marked texts being identified as originally ironic. It was also observed that the
P4 pattern was the one that most identified irony, achieving expressive numbers
in the three variations, standing out among the six linguistic patterns. This
proves that in most cases, Spanish speakers use this language feature to express
irony.
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Table 2. Confusion Matrix with Training Set.
Real Class Real Class
Not Ironic Ironic Score Not Ironic Ironic Score
Predicted Class
Predicted Class
Not Ironic 966 634 1600 Not Ironic 1017 583 1600
(a) (b)
Ironic 470 330 800 Ironic 519 281 800
Score 1436 964 2400 Score 1536 864 2400
Cuba. Mexico.
Real Class
Not Ironic Ironic Score
Predicted Class
Not Ironic 1094 506 1600
(c)
Ironic 557 243 800
Score 1651 749 2400
Spain.
5 Test set
After the analysis with the training set, we applied the patterns in test set.
This database contains: code identifier (ID) and the message to be analyzed.
This time, the texts were not annotated for ironic or non-ironic. The results are
shown in the Table 3.
The P4 repeated the result of the training set, and identified irony with a
great prominence in relation to the others, being able to identify a large group
of texts in the three variations. Second with the best result was the P6 and the
third was the P5.
Although P3 does not present a significant result, we still believe that −
given the intuition of language users − that it would be possible to find better
results in other.
Table 3 presents the final average obtained in the analyzes of the test set, as
well as the average of other resources used by the authors of Irony Corpus [5],[2].
Table 3. F1-score with Test Set.
ES MX CU AVG
LDSE 0.6795 0.6608 0.6335 0.6579
W2V 0.6823 0.6271 0.6033 0.6376
Word nGrams 0.6696 0.6196 0.5684 0.6192
Our Approach 0.5088 0.5464 0.5620 0.5391
MAJORITY 0.4000 0.4000 0.4000 0.4000
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6 Final remarks
During this work, it was noted that irony is expressed in complex ways, and
in similar ways even with different languages. Although intelligent methods are
used for this analysis, irony is shown as a major challenge.
Interpreting, understanding and detecting social networks requires multi-
analysis on linguistic concepts and understanding of how people use virtual
communication. It is concluded that the linguistic patterns established by the
study offer satisfactory results, in addition to allowing the detection in more
than one language, demonstrating a high similarity between the structures of
different languages.
Based on observing language behavior in other corpora, it is possible that
new patterns can be added to the UFPelRules, as well as some existing ones can
be excluded if they provide irrelevant results.
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