Figurative language is one of the most di cult 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 di erent linguistic variants. Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). IberLEF 2019, 24 September 2019, Bilbao, Spain.
Usually, irony is known as a way of communicating the opposite of the literal
meaning [
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 de nition of irony. [
The elaboration of patterns involving possible evidence of ironic declarations
considers the following elements: syntactic rules, static expressions, lists of
laughter expressions, speci c scores, and symbolic language. The implementation of
the proposed patterns is based on the work of [
Six linguistic patterns classi ed 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
The detection of irony has been increasingly worked and explored by the scienti c community.
The use of linguistic patterns to detect irony has already been used by other
researchers, such as [
[
[
List of patterns were built in the three varieties of the Spanish language that
are present in the corpus: Mexican, Spanish and Cuban [
P1- List of Laughter Expression: Social network's users, like twitters, usually use expressions like \jajaja" and \hehehe" to express that they are kidding or to reverse the utterance's meaning. For example, \Jajajajajajajajaja tendencioso, a mi primero que me explique la falta de capacidad de sus candidatos y luego hablamos de con icto de intereses.".
P2 - \No te creas", \No es cierto", \Es broma": In Brazilian Portuguese, people use the expression \So que na~o" 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 - ...<EXPR>! : Expressions accompanied by an exclamation point after an ellipsis are considered ironies, because the punctuation (...) indicates that the phrase is over. However, when such a pattern is used, it would be interpreted as an attempt by the author to add something, nish his sentence, which is supposed to be an irony. For example, \Ojala Carmen Calvo saliendo a decir `Pero no os habeis dado cuenta? Todo este tiempo... el relator... eras tu!' 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 - * j * j !* j ?* j ** j ** j !*?* j ?*!* : People end up not using more nal 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 gured sense. For example: \Entendi `perfectamente' , ms claro no pudieron ser. Que cosa es servicio de mensajeria ??". 4
In this work, we used the training set, available in IroSvA. This database contains 7200 texts with the following annotation: identi cation code (ID), topic (text subject), classi cation of ironic or non-ironic.
UFPelRules classi ed 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 identi ed and marked more texts as ironic in Table 2a and 2b, and less in Table 2c, in relation to those prede ned by the training set.
These results demonstrate that the tool can detect patterns, with half of the marked texts being identi ed as originally ironic. It was also observed that the P4 pattern was the one that most identi ed 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. After the analysis with the training set, we applied the patterns in test set. This database contains: code identi er (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 identi ed 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 signi cant result, we still believe that given the intuition of language users that it would be possible to nd better results in other.
Table 3 presents the nal average obtained in the analyzes of the test set, as
well as the average of other resources used by the authors of Irony Corpus [
During this work, it was noted that irony is expressed in complex ways, and in similar ways even with di erent languages. Although intelligent methods are used for this analysis, irony is shown as a major challenge.
Interpreting, understanding and detecting social networks requires multianalysis on linguistic concepts and understanding of how people use virtual communication. It is concluded that the linguistic patterns established by the study o er satisfactory results, in addition to allowing the detection in more than one language, demonstrating a high similarity between the structures of di erent 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.