A comparison of emotion annotation schemes and a new annotated data set

While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of more nuanced affect has received less attention, and in particular, there are very few publicly available gold standard annotated resources. To address this lack, we present a series of emotion annotation studies on tweets culminating in a publicly available collection of 2,019 tweets with scores on four emotion dimensions: valence, arousal, dominance and surprise, following the emotion representation model identified by Fontaine et.al. (Fontaine et al., 2007). Further, we make a comparison of relative vs. absolute annotation schemes. We find improved annotator agreement with a relative annotation scheme (comparisons) on a dimensional emotion model over a categorical annotation scheme on Ekman’s six basic emotions (Ekman et al., 1987), however when we compare inter-annotator agreement for comparisons with agreement for a rating scale annotation scheme (both with the same dimensional emotion model), we find improved inter-annotator agreement with rating scales, challenging a common belief that relative judgements are more reliable.We would like to thank volunteers from the Insight Centre for Data Analytics for their efforts in pilot study annotations. This work was supported in part by the Science Foundation Ireland under Grant Number 16/IFB/4336 and Grant Number SFI/12/RC/2289 (Insight). The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 644632 (MixedEmotions).non-peer-reviewe

NUIG at EmoInt-2017: BiLSTM and SVR ensemble to detect emotion intensity

This paper describes the entry NUIG in the WASSA 20171 shared task on emotion recognition. The NUIG system used an SVR (SVM regression) and BiLSTM ensemble, utilizing primarily n-grams (for SVR features) and tweet word embeddings (for BiLSTM features). Experiments were carried out on several other candidate features, some of which were added to the SVR model. Parameter selection for the SVR model was run as a grid search whilst parameters for the BiLSTM model were selected through a non-exhaustive ad-hoc search.This work was supported in part by the European Union supported project MixedEmotions (H2020- 644632) and the Science Foundation Ireland under Grant Number SFI/12/RC/2289 (Insight)

Reston

https://commons.lib.jmu.edu/sull/1846/thumbnail.jp

Structural paradox in submonolayer chlorine coverage on Au(111)

Équipe 102 : Surfaces et SpectroscopiesInternational audienceIn this work, we present a combined low-temperature scanning tunneling microscopy (STM) and density functional theory (DFT) study of chlorine adsorption on Au(111) at low coverages. Our STM study of Cl/Au(111) system has shown that at submonolayer coverages (theta < 0.1 ML) chlorine atoms form chainlike structures with abnormally short distances of 3.8 angstrom between them. Our DFT calculations have shown that chlorine atoms can interact with each other through distortion of the substrate and this indirect elastic interaction is strong enough to affect their arrangement in the chainlike structures

MixedEmotions: An open-source toolbox for multi-modal emotion analysis

Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated systems such as call-center operations, recommendations, and assistive technologies. However, to date, adding this functionality was a tedious, costly, and time consuming effort, and one should look for different tools that suits one's needs, and should provide different interfaces to use those tools. The MixedEmotions toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standardized test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.peer-reviewe

Self-Organization of Gold Chloride Molecules on Au(111) Surface

Adsorption of molecular chlorine on Au(111) has been studied with a low-temperature (5 K) scanning tunneling microscope in combination with density functional theory calculations. The formation of AuCl₂ quasi-molecules was detected at chlorine coverage exceeding 0.33 ML. The self-organization of the AuCl₂ species into the ordered “honeycomb” structure was clearly demonstrated for coverages close to saturation