Are Word Embedding-based Features Useful for Sarcasm Detection?

This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.Comment: The paper will be presented at Conference on Empirical Methods in Natural Language Processing (EMNLP) 2016 in November 2016. http://www.emnlp2016.net

IRADABE: Adapting English Lexicons to the Italian Sentiment Polarity Classification task

International audienceInterest in the Sentiment Analysis task has been growing in recent years due to the importance of applications that may benefit from such kind of information. In this paper we addressed the polarity classification task of Italian tweets by using a supervised machine learning approach. We developed a set of features and used them in a machine learning system in order to decide if a tweet is subjective or objective. The polarity result itself was then used as an additional feature to determine whether a tweet contains ironical content or not. We faced the lack of resources in Italian by translating (mostly automatically) existing resources for the English language. Our model obtained good results in the SentiPolC 2014 task, being one of the best ranked systems.L'interesse nell'analisi automatica dei sentimenti è continuamente cresciuto negli ultimi anni per via dell'importanza delle applicazioni in cui questo tipo di analisi può essere utilizzato. In quest'articolo descriviamo gli esperimenti portati a termine nel campo della classificazione della polarità di tweets scritti in italiano, usando un approccio basato sull'apprendimento automatico. Un certo numero di criteri è stato utilizzato come features per assegnare una polarità e quindi determinare se i tweets contengono dell'ironia o meno. Per questi esperimenti, la mancanza di risorse (in particolare di dizionari specializzati) è stata compensata adattando delle risorse esistenti per la lingua inglese, in gran parte utilizzando delle tecniche di traduzione automatica. Il modello così ottenutò e stato uno dei migliori nel task SentiPolC a Evalita 2014

Sarcasm Detection in English and Arabic Tweets Using Transformer Models

This thesis describes our approach toward the detection of sarcasm and its various types in English and Arabic Tweets through methods in deep learning. There are five problems we attempted: (1) detection of sarcasm in English Tweets, (2) detection of sarcasm in Arabic Tweets, (3) determining the type of sarcastic speech subcategory for English Tweets, (4) determining which of two semantically equivalent English Tweets is sarcastic, and (5) determining which of two semantically equivalent Arabic Tweets is sarcastic. All tasks were framed as classification problems, and our contributions are threefold: (a) we developed an English binary classifier system with RoBERTa, (b) an Arabic binary classifier with XLM-RoBERTa, and (c) an English multilabel classifier with BERT. Pre-processing steps are taken with labeled input data prior to tokenization, such as extracting and appending verbs/adjectives or representative/significant keywords to the end of an input tweet to help the models better understand and generalize sarcasm detection. We also discuss the results of simple data augmentation techniques to improve the quality of the given training dataset as well as an alternative approach to the question of multilabel sequence classification. Ultimately, our systems place us in the top 14 participants for each of the five tasks in a sarcasm detection competition

Sarcasm Detection and User Behaviour Analysis

Sarcasm is a sort of sentiment where public expresses their negative emotions using positive word within the text. It is very tough for humans to acknowledge. In this way we show the interest in sarcasm detection of social media text, particularly in tweets. In this paper we propose new method pattern based approach for sarcasm detection, and also used behavioral modelling approach for effective sarcasm detection by analyzing the content of tweets however by conjoint exploiting the activity traits of users derived from their past activities. In this way we propose the different method for sarcasm detection such as, Sentiment-related Features, Punctuation-Related Features, Syntactic and Semantic Features, Pattern-Related Features approach for detection of sarcasm in the tweet. We also develop the behavioural modeling approach to check the user emotion and sentiment analysis. By using the various classifiers such as TREE, Support Vector Machine (SVM), BOOST and Maximum Entropy, we check the accuracy and performance. Our proposed approach reaches an accuracy of 94 %

Détection automatique de l'ironie dans les tweets en français

International audienceCet article présente une méthode par apprentissage supervisé pour la détection de l'ironie dans les tweets en français. Un classifieur binaire utilise des traits de l'état de l'art dont les performances sont reconnues, ainsi que de nouveaux traits issus de notre étude de corpus. En particulier, nous nous sommes intéressés à la négation et aux oppositions explicites/implicites entre des expressions d'opinion ayant des polarités différentes. Les résultats obtenus sont encourageants

Иронические коннотации в устойчивых сочетаниях с упоминанием ученых

Шунейко, А. А. Иронические коннотации в устойчивых сочетаниях с упоминанием ученых / А. А. Шунейко, О. В. Чибисова // Научный результат. Сер. Вопросы теоретической и прикладной лингвистики. - 2019. - Т.5, №4.-С. 32-45. - Doi: 10.18413/2313-8912-2019-5-4-0-4.Целью работы стало детальное выяснение того, каким образом, на каких основаниях и с приращением каких смыслов в русском языке осуществляются иронические наименования представителей науки. Выяснены причины, по которым в русском языке присутствует такое количество номинаций именно ученых, а не какой-либо иной социальной групп