Using Lexical Resources for Irony and Sarcasm Classification

Mladenović, Marko Z.; Krstev, Cvetana; Mitrović, Jelena; Stanković, Ranka

doi:10.1145/3136273.3136298

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…Mladenovic et al [ 27 ] presented a model that uses various language resources: morphological dictionaries, sentiment lexicon, lexicon of markers, and a WordNet-based ontology or classification of statements into ironic and non-ironic. The authors performed the evaluation on two collections of tweets that had been manually annotated according to irony.…”

Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Júnior

Albuquerque

Andrade

et al. 2020

Sensors

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Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

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Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Júnior

Albuquerque

Andrade

et al. 2020

Sensors

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“…This ontology-based method for detection of the rhetorical figure simile achieved 51.8% accuracy. For automatic detection of irony, a set of over four hundred reasoning SWRL rules for extracting indirect antonymous pairs of words were defined [47]. The proposed model was evaluated (achieved accuracy 86.1%) on a collection of 1,732 tweets that had been manually annotated according to irony.…”

Section: Ontology-based Rhetorical Figures Detectionmentioning

confidence: 99%

“…Research presented in [34,47,60] and [15] pointed out the importance of literary devices near to the candidate figure (called either features, constraints or markers) which indicate the presence of the figure. For example, in [47] stylistic irony markers like punctuation marks, exclamation points, and italicized letters were used to indicate the existence of irony. In the previous tweet example, stylistic markers are the presence of emphasis "baš" (O, how) and the exclamation point.…”

Section: Ontology-based Rhetorical Figures Detectionmentioning

confidence: 99%

“…Knowledge-based systems can be broadly divided into two areas: "Knowledge models -formalisms for knowledge representation and reasoning, and Reasoning systemssoftware that generates conclusions from available knowledge using logical techniques such as deduction and induction" [3]. Automatic detection of rhetorical figures in text and their annotation based on machine learning and ontological approaches has been explored for metaphors [29,43,59], epanaphora [60], antimetabole [15] metonymy [20,40,50], irony [9,47,63], sarcasm [13,23] and other figures [22,34], but not enough attention has been given to using these approaches for enhancement of argument mining systems.…”

Section: Introductionmentioning

confidence: 99%

“…The first of them describes using a formal domain ontology of rhetorical figures for Serbian (The RetFig ontology) [48] and Serbian WordNet-based ontology (SWN) [37] in developing tools for automated detection of rhetorical figures in Serbian [46,47]. And the second is the Lassoing Rhetoric project that takes a text input and produces a knowledge base output that contains meaning representations of the text including inferred knowledge based on the rules that are applied via the SWRL processing phase [51].…”

Section: Introductionmentioning

confidence: 99%

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Ontological representations of rhetorical figures for argument mining

Mitrović

O'Reilly

Mladenović

et al. 2017

AAC

Self Cite

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Abstract. This paper surveys ontological modeling of rhetorical concepts, developed for use in argument mining and other applications of computational rhetoric, projecting their future directions. We include ontological models of argument schemes applying Rhetorical Structure Theory (RST); the RhetFig proposal for modeling; the related RetFig Ontology of Rhetorical Figures for Serbian (developed by two of the authors); and the Lassoing Rhetoric project (developed by another of the authors). The Lassoing Rhetoric venture is interesting for its multifaceted approach to linguistic devices, prominently including rhetorical figures, but also RST relations and stylistic models, like the use of historic present. This application takes a natural language text input and uses syntactic parsing tools to produce a knowledge base of linguistic entities using references to an OWL ontological framework, locating these devices using Semantic Web Rule Language (SWRL) logic rules. The paper also reports on a similar approach in research into detecting ironic tweets in a Serbian twitter corpus. The rhetorical schemes used for argument mining are also presented, as well as some suggestions for novel argument schemes based on the ontological approach to rhetorical figuration.

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