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In recent years, there has been a proliferation of opinion-heavy texts on the Web: opinions of Internet users, comments on social networks, etc. Automating the synthesis of opinions has become crucial to gaining an overview on a given topic. Current automatic systems perform well on classifying the subjective or objective character of a document. However, classifications obtained from polarity analysis remain inconclusive, due to the algorithms' inability to understand the subtleties of human language. Automatic Detection of Irony presents, in three stages, a supervised learning approach to predicting whether a tweet is ironic or not. The book begins by analyzing some everyday examples of irony and presenting a reference corpus. It then develops an automatic irony detection model for French tweets that exploits semantic traits and extralinguistic context. Finally, it presents a study of portability in a multilingual framework (Italian, English, Arabic).
In recent years, there has been a proliferation of opinion-heavy texts on the Web: opinions of Internet users, comments on social networks, etc. Automating the synthesis of opinions has become crucial to gaining an overview on a given topic. Current automatic systems perform well on classifying the subjective or objective character of a document. However, classifications obtained from polarity analysis remain inconclusive, due to the algorithms' inability to understand the subtleties of human language. Automatic Detection of Irony presents, in three stages, a supervised learning approach to predicting whether a tweet is ironic or not. The book begins by analyzing some everyday examples of irony and presenting a reference corpus. It then develops an automatic irony detection model for French tweets that exploits semantic traits and extralinguistic context. Finally, it presents a study of portability in a multilingual framework (Italian, English, Arabic).
Seminar paper from the year 2010 in the subject Speech Science / Linguistics, grade: 1,0, Saarland University (Computerlinguistik), course: Computational Approaches to Creative Language, language: English, abstract: Human communication often involves the use of irony. In many cases, it is far from obvious if an utterance is meant ironical or not. Context and world knowledge are needed to discriminate literal from ironic intent. Linguists have worked on describing the nature of irony and come up with ideas which reflect the intuitive understanding of irony. Parallely, computational linguists are confronted with the challenge of automatically detecting irony. When an utterance contains irony, the only chance of getting the intent, is understanding and interpreting the irony in it. I review different theories of irony in chapter 2. Chapter 3 describes the state-of-the-art of automatic irony detection, covers the importance of corpus study for future research and proposes a fusion between theory, corpus study and automatic detection.
This open access volume constitutes the refereed proceedings of the 27th biennial conference of the German Society for Computational Linguistics and Language Technology, GSCL 2017, held in Berlin, Germany, in September 2017, which focused on language technologies for the digital age. The 16 full papers and 10 short papers included in the proceedings were carefully selected from 36 submissions. Topics covered include text processing of the German language, online media and online content, semantics and reasoning, sentiment analysis, and semantic web description languages.
The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network analysis - Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics - Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies - Provides insights into opinion spamming, reasoning, and social network mining - Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences - Serves as a one-stop reference for the state-of-the-art in social media analytics
This book constitutes the proceedings of the 14th International Conference on Computational Processing of the Portuguese Language, PROPOR 2020, held in Evora, Portugal, in March 2020. The 36 full papers presented together with 5 short papers were carefully reviewed and selected from 70 submissions. They are grouped in topical sections on speech processing; resources and evaluation; natural language processing applications; semantics; natural language processing tasks; and multilinguality.
Irony in Language and Thought assembles an interdisciplinary collection of seminal empirical and theoretical papers on irony in language and thought into one comprehensive book. A much-needed resource in the area of figurative language, this volume centers on a theme from cognitive science - that irony is a fundamental way of thinking about the human experience. The editors lend perspective in the form of opening and closing chapters, which enable readers to see how such works have furthered the field, as well as to inspire present and future scholars. Featured articles focus on the following topics: theories of irony, addressing primarily comprehension of its verbal form context in irony comprehension social functions of irony the development of irony understanding situational irony. Scholars and students in psychology, linguistics, philosophy, literature, anthropology, artificial intelligence, art, and communications will consider this book an excellent resource. It serves as an ideal supplement in courses that present major ideas in language and thought.
Seminar paper from the year 2010 in the subject Speech Science / Linguistics, grade: 1,0, Saarland University (Computerlinguistik), course: Computational Approaches to Creative Language, language: English, abstract: Human communication often involves the use of irony. In many cases, it is far from obvious if an utterance is meant ironical or not. Context and world knowledge are needed to discriminate literal from ironic intent. Linguists have worked on describing the nature of irony and come up with ideas which reflect the intuitive understanding of irony. Parallely, computational linguists are confronted with the challenge of automatically detecting irony. When an utterance contains irony, the only chance of getting the intent, is understanding and interpreting the irony in it. I review different theories of irony in chapter 2. Chapter 3 describes the state-of-the-art of automatic irony detection, covers the importance of corpus study for future research and proposes a fusion between theory, corpus study and automatic detection.
This book constitutes the proceedings of the 22nd International Conference on Speech and Computer, SPECOM 2020, held in St. Petersburg, Russia, in October 2020. The 65 papers presented were carefully reviewed and selected from 160 submissions. The papers present current research in the area of computer speech processing including speech science, speech technology, natural language processing, human-computer interaction, language identification, multimedia processing, human-machine interaction, deep learning for audio processing, computational paralinguistics, affective computing, speech and language resources, speech translation systems, text mining and sentiment analysis, voice assistants, etc. Due to the Corona pandemic SPECOM 2020 was held as a virtual event.