Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

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