Delia Irazú Hernández Farías

Figurative messages and affect in Twitter

The use of irony and sarcasm has been proven to be a pervasive phenomenon in social media posing a challenge to sentiment analysis systems. Such devices, in fact, can influence and twist the polarity of an utterance in different ways. A new dataset of over 10,000 tweets including a high variety of figurative language types, manually annotated with sentiment scores, has been released in the context of the task 11 of SemEval-2015. In this paper, we propose an analysis of the tweets in the dataset to investigate the open research issue of how separated figurative linguistic phenomena irony and sarcasm are, with a special focus on the role of features related to the multi-faceted affective information expressed in such texts. We considered for our analysis tweets tagged with #irony and #sarcasm, and also the tag #not, which has not been studied in depth before. A distribution and correlation analysis over a set of features, including a wide variety of psycholinguistic and emotional features, suggests arguments for the separation between irony and sarcasm. The outcome is a novel set of sentiment, structural and psycholinguistic features evaluated in binary classification experiments. We report about classification experiments carried out on a previously used corpus for #irony vs #sarcasm. We outperform in terms of F-measure the state-of-the-art results on this dataset. Overall, our results confirm the difficulty of the task, but introduce new data-driven arguments for the separation between #irony and #sarcasm. Interestingly, #not emerges as a distinct phenomenon.

Irony Detection in Twitter: The Role of Affective Content

Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.

ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only wordsentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets.

Friends and Enemies of Clinton and Trump: Using Context for Detecting Stance in Political Tweets

Stance detection, the task of identifying the speaker’s opinion towards a particular target, has attracted the attention of researchers. This paper describes a novel approach for detecting stance in Twitter. We define a set of features in order to consider the context surrounding a target of interest with the final aim of training a model for predicting the stance towards the mentioned targets. In particular, we are interested in investigating political debates in social media. For this reason we evaluated our approach focusing on two targets of the SemEval-2016 Task 6 on Detecting stance in tweets, which are related to the political campaign for the 2016 U.S. presidential elections: Hillary Clinton vs. Donald Trump. For the sake of comparison with the state of the art, we evaluated our model against the dataset released in the SemEval-2016 Task 6 shared task competition. Our results outperform the best ones obtained by participating teams, and show that information about enemies and friends of politicians help in detecting stance towards them.

Sentiment Polarity Classification of Figurative Language: Exploring the Role of Irony-Aware and Multifaceted Affect Features

The presence of figurative language represents a big challenge for sentiment analysis. In this work, we address the task of assigning sentiment polarity to Twitter texts when figurative language is employed, with a special focus on the presence of ironic devices. We introduce a pipeline model which aims to assign a polarity value exploiting, on the one hand, irony-aware features, which rely on the outcome of a state-of-the-art irony detection model, on the other hand a wide range of affective features that cover different facets of affect exploiting information from various sentiment and emotion lexical resources for English available to the community, possibly referring to different psychological models of affect. The proposed method has been evaluated on a set of tweets especially rich in figurative language devices proposed as a benchmark in the shared task on “Sentiment Analysis of Figurative Language” at SemEval-2015. Experiments and results of feature ablation show the usefulness of irony-aware features and the impact of using different affective lexicons for the task.

Universality and Creativity: The Usage of Language in Gender and Irony

Author profiling deals with distinguishing between classes of authors rather than individual authors on the basis of their usage of language. What is much more subjective in terms of usage of language is when authors employ irony as linguistic device. The aim of this paper is to introduce the reader to concepts such as universality of language among classes of authors, e.g. of the same gender, and creativity in irony.