ANALYSIS OF IDIOMATIC EMOTION EXPRESSIONS DETECTED FROM ONLINE MOVIE REVIEWS

A large number of idiomatic emotion expressions in Korean are composed of certain nouns of human body parts accompanied by selected predicates, which represent a ‘physiological metonymy’ of sentiment (Lakoff 1987, Ungerer & Schmid 1996)or instance, kasum-i ttwita literally means a physiological reaction (i.e. one’s heart beat) but also can represent the emotion like being thrilled to bits. We compared idiomatic emotion expressions used in English online movie reviews and those observed in Korean, and noticed that the nouns of body parts such as kasum ‘heart’, maum ‘mind’ or nwun ‘eyes’ emerge frequently in both languages, whereas ekkay ‘shoulder’, kancang ‘intestines’ or ppye ‘bones’ seem to be rather reserved for Korean emotion expressions. In this study, we extract idiomatic emotion expressions based on the 13 nouns of body parts listed by Lim (2001) from Korean online movie reviews. For instance, nouns such as meli ‘head’, ip ‘mouth’ or simcang ‘cardia’ are frequently used for constituting the emotion expressions of POSITIVE values as shown in ip-ul tamwul-swu epsta ‘be with open mouth (with delight) these nouns hardly occur in NEGATIVE emotion expressions, which is not predictable from their semantic features, but reveals their lexical idiosyncrasy. The frequent emotion expressions observed in online movie reviews will be analyzed and classified according to their semantic properties. We will show what salient traits of Korean emotion expressions can be remarked in current online subjective documents such as users’ reviews, blogs or opinion texts

Emotions in the face: biology or culture? – Using idiomatic constructions as indirect evidence to inform a psychological research controversy

Research on the facial expression of emotions has become a bone of contention in psychological research. On the one hand, Ekman and his colleagues have argued for a universal set of six basic emotions that are recognized with a considerable degree of accuracy across cultures and automatically displayed in highly similar ways by people. On the other hand, more recent research in cognitive science has provided results that are supportive of a cultural-relativist position. In this paper this controversy is approached from a contrastive perspective on phraseological constructions. It focuses on how emotional displays are codified in somatic idioms in some European (English, German, French, Spanish) and East Asian (Japanese, Korean, Chinese [Cantonese]) languages. Using somatic idioms such as make big eyes or die Nase rümpfen as a pool of evidence to shed linguistic light on the psychological controversy, the paper engages with the following general research question: Is there a significant difference between European and East Asian somatic idioms or do these constructions rather speak for a universal apprehension of facial emotion displays? To answer this question, the paper compares somatic expressions that are selected from (idiom) dictionaries of the languages listed above. Moreover, native speakers of the East Asian languages were consulted to support the analysis of the respective data. All corresponding entries were analysed categorically, i. e. with regard to whether or not they encode a given facial area to denote a specific emotion. The results show arguments both for and against the universalist and the cultural-relativist positions. In general, they speak for an opportunistic encoding of facial emotion displays

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Sentiment Polarity Classification of Comments on Korean News Articles Using Feature Reweighting

일반적으로 인터넷 신문 기사에 대한 댓글은 그 신문 기사에 대한 주관적인 감정이나 의견을 포함하고 있다. 따라서 이런 신문 기사의 댓글에 대한 감정을 인식하고 분류하는 데에는 그 신문 기사의 원문 내용이 중요한 영향을 미친다. 이런 점에 착안하여 본 논문은 기사의 원문 내용과 감정 사전을 이용하는 가중치 조정 방법을 제안하고, 제안된 가중치 조정 방법을 이용해서 한국어 신문 기사의 댓글에 대한 감정 이진 분류 방법을 제안한다. 가중치 조정 방법에는 다양한 자질 집합이 사용되는데 그것은 댓글에 포함된 감정 단어, 그리고 감정 사전과 뉴스 기사의 본문에 관련된 자질들, 마지막으로 뉴스 기사의 카테고리 정보가 포함되어 있다. 여기서 말하는 감정 사전은 한국어 감정 사전을 의미하며 아직 공개된 것이 없기 때문에, 기존에 있는 영어 감정 사전을 이용하여 구축하였다. 본 논문에서 제안된 감정 이진 분류는 기계 학습을 이용한다. 일반적으로 기계 학습을 위해서는 학습 말뭉치가 필요한데 특별히 감정 분류 문제에서는 긍정 혹은 부정 감정 태그가 부착된 말뭉치가 필요하다. 이 말뭉치의 경우도, 공개된 한국어 감정 말뭉치가 아직 없기 때문에 말뭉치를 직접 구축하였다. 사용된 기계 학습 방법으로는 Na&iumlve Bayes, k-NN, SVM이 있고, 자질 선택 방법으로는 Document Frequency, χ^2 statistic, Information Gain이 있다. 그 결과, 댓글 안에 포함된 감정 단어와 그 댓글에 대한 기사 본문이 감정 분류에 매우 효과적인 자질임을 확인할 수 있었다.Chapter 1 Introduction 1 Chapter 2 Related Works 4 2.1 Sentiment Classification 4 2.2 Feature Weighting in Vector Space Model 5 2.3 Feature Extraction and Selection 7 2.4 Classifiers 10 2.5 Accuracy Measures 14 Chapter 3 Feature Reweighting 16 3.1 Feature extraction in Korean 16 3.2 Feature Reweighting Methods 17 3.3 Examples of Feature Reweighting Methods 18 Chapter 4 Sentiment Polarity Classification System 21 4.1 Model Generation 21 4.2 Sentiment Polarity Classification 23 Chapter 5 Data Preparation 25 5.1 Korean Sentiment Corpus 25 5.2 Korean Sentiment Lexicon 27 Chapter 6 Experiments 29 6.1 Experimental Environment 29 6.2 Experimental Results 30 Chapter 7 Conclusions and Future Works 38 Bibliography 40 Acknowledgments 4

Using High Dimensional Computing on Arabic Language Speech to Text Classification

High-Dimensional Processing is the idea that mind register illustrations of neural activities which are not immediately related with numbers. The objective of the article is hyper- dimensional computation of data for categorization of text from two distinct speech datasets, namely the Arabic Corpus dataset and the MediaSpeech dataset with four languages (Arabic, Spanish, French, and Turkish). Through the use of an n-gram encoding scheme, hyper dimensional computing is used to conduct the analysis from the prior set of data. Using hyper dimensional computing, the MediaSpeech dataset accomplishes 100% accuracy for all 4-gram to 14-gram encoding schemes, while the Arabic Corpus dataset accomplishes 100% accuracy for 4-gram to 7-gram encoding schemes