Positivity of the English language

Over the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languages neutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of word positivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency of word use.Comment: Manuscript: 9 pages, 3 tables, 5 figures; Supplementary Information: 12 pages, 3 tables, 8 figure

Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.Comment: 11 pages, to appear at ACM MM'1

Evaluative Language and Its Solidarity-Building Role on TED.com: An Appraisal and Corpus Analysis

Language is a key resource in the formation of online communities, which are in turn central to an understanding of contemporary social relations. This study looks at TED.com, an educational video-hosting platform with few in-built community-building functionalities, to explore the potential for users to affiliate through their language choices. Grounded in Systemic Functional Linguistics, the study uses the Appraisal framework, extended using corpus linguistic methods, in order to analyse users’ reactions to TED videos. The study shows that online participants use evaluative language to align with certain ideas and, based on these affinities, form affiliations characterized by sociability and solidarity. These affiliations raise important questions about the conception of ‘community’ in twenty-first century society

A Levinasian Reconstruction of the Political Significance of Vulnerability

The concept of vulnerability has been renewed in meaning and importance over recent decades. Scholars such as Judith Butler, Martha Fineman and Pamela Sue Anderson have endeavored to redeem vulnerability from its traditional signification as a negative individual condition, and to reveal the positive meaning of vulnerability as a transformative call for solidarity, equality and love. In this paper we examine the newly constructed positive understanding of vulnerability, and argue that the current way of pursuing this positive understanding affirms a merely functional positivity. In the recent accounts, vulnerability is still a status to avoid, yet functions positively as a corrective force to the environment that produces vulnerability. We will try to find an essential way of designating the positivity of vulnerability by revisiting the philosophical discussion on vulnerability in Emmanuel Levinas. We will argue that Levinas’s notion of vulnerability is positive in a sense which goes beyond the purely functional; it is seen as essential for his definition of humanity. Yet compared to the contemporary discussion, Levinas’s notion of vulnerability lacks a direct social political meaning. We will tentatively explore how the essential positivity drawn from Levinas can provide a new way to construct the political significance of vulnerability

The emotional arcs of horror: a distant reading of Stephen King’s novels

Sentiment analysis, the computational inference of emotion in text through Natural Language Processing, is increasingly used to analyze social and cultural trends. In this thesis, we create narrative time-series and word-shift graphs for each of Stephen King’s novels using the Hedonometer, quantifying the lexical changes responsible for emotional arcs found in each story. Our results suggest King’s work has increasingly shifted in genre from horror to science fiction. The work contributes to a growing science of stories being developed by the Computational Story Lab

Judgements of a speaker’s personality are correlated across differing content and stimulus type

It has previously been shown that first impressions of a speaker’s personality, whether accurate or not, can be judged from short utterances of vowels and greetings, as well as from prolonged sentences and readings of complex paragraphs. From these studies, it is established that listeners’ judgements are highly consistent with one another, suggesting that different people judge personality traits in a similar fashion, with three key personality traits being related to measures of valence (associated with trustworthiness), dominance, and attractiveness. Yet, particularly in voice perception, limited research has established the reliability of such personality judgements across stimulus types of varying lengths. Here we investigate whether first impressions of trustworthiness, dominance, and attractiveness of novel speakers are related when a judgement is made on hearing both one word and one sentence from the same speaker. Secondly, we test whether what is said, thus adjusting content, influences the stability of personality ratings. 60 Scottish voices (30 females) were recorded reading two texts: one of ambiguous content and one with socially-relevant content. One word (~500 ms) and one sentence (~3000 ms) were extracted from each recording for each speaker. 181 participants (138 females) rated either male or female voices across both content conditions (ambiguous, socially-relevant) and both stimulus types (word, sentence) for one of the three personality traits (trustworthiness, dominance, attractiveness). Pearson correlations showed personality ratings between words and sentences were strongly correlated, with no significant influence of content. In short, when establishing an impression of a novel speaker, judgments of three key personality traits are highly related whether you hear one word or one sentence, irrespective of what they are saying. This finding is consistent with initial personality judgments serving as elucidators of approach or avoidance behaviour, without modulation by time or content. All data and sounds are available on OSF (osf.io/s3cxy)