Antonio Reyes

A multidimensional approach for detecting irony in Twitter

Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or “tweets”. Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. “Toyota”) and user-generated tags (e.g. “#irony”). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.

Making objective decisions from subjective data: Detecting irony in customer reviews

The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony, in particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents that trigger a chain reaction in people. The findings were assessed employing three classifiers. Initial results are largely positive, and provide valuable insights into the subjective issues of language facing tasks such as sentiment analysis, opinion mining and decision making.

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.

On the difficulty of automatically detecting irony: beyond a simple case of negation

It is well known that irony is one of the most subtle devices used to, in a refined way and without a negation marker, deny what is literally said. As such, its automatic detection would represent valuable knowledge regarding tasks as diverse as sentiment analysis, information extraction, or decision making. The research described in this article is focused on identifying key values of components to represent underlying characteristics of this linguistic phenomenon. In the absence of a negation marker, we focus on representing the core of irony by means of three conceptual layers. These layers involve 8 different textual features. By representing four available data sets with these features, we try to find hints about how to deal with this unexplored task from a computational point of view. Our findings are assessed by human annotators in two strata: isolated sentences and entire documents. The results show how complex and subjective the task of automatically detecting irony could be.

The impact of semantic and morphosyntactic ambiguity on automatic humour recognition

Humour is one of the most amazing characteristics that defines us as human beings and social entities. Its study supposes a deep insight into several areas such as linguistics, psychology or philosophy. From the Natural Language Processing (NLP) perspective, recent researches have shown that humour can be automatically generated and recognized with some success. In this work we present a study carried out on a collection of English texts in order to investigate whether or not semantic and morphosyntactic ambiguities may be employed as features in the automatic humour recognition task. The results we have obtained show that it is possible to discriminate humorous from non humorous sentences through features like perplexity or sense dispersion.

An Analysis of the Impact of Ambiguity on Automatic Humour Recognition

One of the most amazing characteristics that defines the human being is humour. Its analysis implies a set of subjective and fuzzy factors, such as the linguistic, psychological or sociological variables that produce it. This is one of the reasons why its automatic processing seems to be not straightforward. However, recent researches in the Natural Language Processing area have shown that humour can automatically be generated and recognised with success. On the basis of those achievements, in this study we present the experiments we have carried out on a collection of Italian texts in order to investigate how to characterize humour through the study of the ambiguity, especially with respect to morphosyntactic and syntactic ambiguity. The results we have obtained show that it is possible to differentiate humorous from non humorous data through features like perplexity or sentence complexity.