Beyond Literal Meanings: Recognition and Analysis of Emotions, Legality and Irony in Microtexts
Date Issued
2015
Date
2015
Author(s)
Tang, Yi-jie
Abstract
The non-literal or non-lexical aspects of communication cannot be interpreted directly and literally. The identification and analysis of real intent beyond literal meanings is a challenging task in natural language processing, especially when working on microtexts such as microblogs that are limited to 140 characters. The recognition and analysis of these components are crucial for many applications including sentiment analysis, opinion mining, question answering and chatterbots. In this study, emotion recognition, online advertising legality identification and verbal irony analysis are examined. In the emotion recognition experiments, the generation of user emotions on a microblogging platform is modeled from both writers’ and readers’ perspectives. Graphic emoticons, which are commonly used to express users’ emotions, serve as emotion labels so that microtext emotion datasets can be constructed. To build classifiers for the emotion identification task, support vector machine (SVM)-based algorithms are adopted. In addition to textual features, non-verbal factors, including social relation, user behavior and relevance degree, are also used as features. The experimental results show that the combination of textual, social and behavioral features can be used to achieve the best emotion-prediction performance. The emotional transitions from the poster to the responder in a conversation are also analyzed and predicted in this study. As online advertising continues to grow, Internet users, advertisers, online advertising platforms and the authorities all have the need to avoid or prevent the issues that false and/or misleading advertisements can potentially cause. Many of these false advertising messages are present in short texts, and their appropriateness cannot be easily interpreted. This problem is addressed by building one-class and two-class classifiers with datasets consisting of short illegal advertising statements published by the government and product descriptions from an online shopping website. The results show that the models using the log relative frequency ratio (logRF) combined with unigrams as features achieve the best performance. The logRF values are also used to mine verb phrases that are typically used in illegal advertisements. These verb phrases can be used as a reference for both the advertisers and the authorities. A web-based false advertisement recognition system was also built in this study using the techniques applied to the above experiments in order to reduce human effort in filtering false advertising messages and help protect Internet users from misleading advertising. In verbal irony, the literal meaning of an utterance can be the opposite of what is actually meant. For simplification, this study focuses on ironic expressions in which negative actual meanings are represented by positive words. Ironic messages in microblogs are infrequent and cannot be identified by simply examining the literal meanings of the words. To construct a Chinese irony corpus, ironic messages are collected from microblogs based on emoticon use, linguistic forms and sentiment polarity through a bootstrapping approach. Five types of irony patterns are found in the collected ironic messages. The structure of ironic expressions is also analyzed, and three types of elements are found to form an ironic expression. A conditional random field (CRF)-based approach is used to automatically identify irony elements and ironic messages and reduce the human effort in the bootstrapping approach of irony pattern discovery.
Subjects
natural language processing
nlp
microblog
microtext
emotion
sentiment
legality
advertising
advertisement
irony
sarcasm
semantics
pragmatics
Type
thesis
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