Figurative Language Detection using Deep Learning and Contextual Features

The size of data shared over the Internet today is gigantic. A big bulk of it comes from postings on social networking sites such as Twitter and Facebook. Some of it also comes from online news sites such as CNN and The Onion. This type of data is very good for data analysis since they are very personalized and specific. For years, researchers in academia and various industries have been analyzing this type of data. The purpose includes product marketing, event monitoring, and trend analysis. The highest usage for this type of analysis is to find out the sentiments of the public about a certain topic or product. This field is called sentiment analysis. The writers of such posts have no obligation to stick to only literal language. They also have the freedom to use figurative language in their publications. Hence, online posts can be categorized into two: Literal and Figurative. Literal posts contain words or sentences that are direct or straight to the point. On the contrary, figurative posts contain words, phrases, or sentences that carry different meanings than usual. This could flip the whole polarity of a given post. Due to this nature, it can jeopardize sentiment analysis works that focus primarily on the polarity of the posts. This makes figurative language one of the biggest problems in sentiment analysis. Hence, detecting it would be crucial and significant. However, the study of figurative language detection is non-trivial. There have been many existing works that tried to execute the task of detecting figurative language correctly, with different methodologies used. The results are impressive but still can be improved. This thesis offers a new way to solve this problem. There are essentially seven commonly used figurative language categories: sarcasm, metaphor, satire, irony, simile, humor, and hyperbole. This thesis focuses on three categories. The thesis aims to understand the contextual meaning behind the three figurative language categories, using a combination of deep learning architecture with manually extracted features and explore the use of well know machine learning classifiers for the detection tasks. In the process, it also aims to describe a descending list of features according to the importance. The deep learning architecture used in this work is Convolutional Neural Network, which is combined with manually extracted features that are carefully chosen based on the literature and understanding of each figurative language. The findings of this work clearly showed improvement in the evaluation metrics when compared to existing works in the same domain. This happens in all of the figurative language categories, proving the framework’s possession of quality

Conceiving God: Literal and Figurative Prompt for a More Tectonic Distinction

John Sanders’ Theology in the Flesh, the first comprehensive overview of the toolkit that contemporary cognitive linguistics offers for theological appropriation, despite its remarkable success, gives rather minimal attention to blending theory, one of the discipline’s most formidable tools. This paper draws on blending theory to offer an alternative to Sanders’ chapter on conceiving God. Central to the proposal is claim that God-talk, like many of the advances in science, technology, and art, entails a kind of tectonic understanding and conceptual mapping that is neither literal nor figurative

Interactivist approach to representation in epigenetic agents

Interactivism is a vast and rather ambitious philosophical and theoretical system originally developed by Mark Bickhard, which covers plethora of aspects related to mind and person. Within interactivism, an agent is regarded as an action system: an autonomous, self-organizing, self-maintaining entity, which can exercise actions and sense their effects in the environment it inhabits. In this paper, we will argue that it is especially suited for treatment of the problem of representation in epigenetic agents. More precisely, we will elaborate on process-based ontology for representations, and will sketch a way of discussing about architectures for epigenetic agents in a general manner

Detecting Emotional Involvement in Professional News Reporters: An Analysis of Speech and Gestures

This study is aimed to investigate the extent to which reporters\u2019 voice and body behaviour may betray different degrees of emotional involvement when reporting on emergency situations. The hypothesis is that emotional involvement is associated with an increase in body movements and pitch and intensity variation. The object of investigation is a corpus of 21 10-second videos of Italian news reports on flooding taken from Italian nation-wide TV channels. The gestures and body movements of the reporters were first inspected visually. Then, measures of the reporters\u2019 pitch and intensity variations were calculated and related with the reporters' gestures. The effects of the variability in the reporters' voice and gestures were tested with an evaluation test. The results show that the reporters vary greatly in the extent to which they move their hands and body in their reportings. Two gestures seem to characterise reporters\u2019 communication of emergencies: beats and deictics. The reporters\u2019 use of gestures partially parallels the reporters\u2019 variations in pitch and intensity. The evaluation study shows that increased gesturing is associated with greater emotional involvement and less professionalism. The data was used to create an ontology of gestures for the communication of emergenc

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research

Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market

The original research question here is given by marketers in general, i.e., how to explain the changes in the desired timescale of the market. Tangled String, a sequence visualization tool based on the metaphor where contexts in a sequence are compared to tangled pills in a string, is here extended and diverted to detecting stocks that trigger changes in the market and to explaining the scenario of contextual shifts in the market. Here, the sequential data on the stocks of top 10 weekly increase rates in the First Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled String. The changing in the prices of stocks is a mixture of various timescales and can be explained in the time-scale set as desired by using TS. Also, it is found that the change points found by TS coincided by high precision with the real changes in each stock price. As TS has been created from the data-driven innovation platform called Innovators Marketplace on Data Jackets and is extended to satisfy data users, this paper is as evidence of the contribution of the market of data to data-driven innovations.Comment: 16 pages and 7 figures. The author started to write this paper as an extension of the paper [20] in the reference list, but the content came to be changed substantially, not by only minor extension but to a new pape