Automatic Sarcasm Detectionreview
Аннотация: Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.
Год издания: 2017
Авторы: Aditya Joshi, Pushpak Bhattacharyya, Mark Carman
Издательство: Association for Computing Machinery
Источник: ACM Computing Surveys
Ключевые слова: Sentiment Analysis and Opinion Mining, Advanced Text Analysis Techniques, Topic Modeling
Другие ссылки: ACM Computing Surveys (HTML)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (PDF)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (HTML)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (PDF)
Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (HTML)
Открытый доступ: green
Том: 50
Выпуск: 5
Страницы: 1–22