Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News

Abstract

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting -in a totally automated way -a highcoverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a naïve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

“…• Yelp Restaurant Sentiment Lexicon -adopted by Chen et al (2017) • Amazon Laptop Sentiment Lexicon -adopted by Chen et al (2017) • Macquarie Semantic Orientation Lexiconadopted by Chen et al (2017) • Harvard's General Inquirer Lexicon -adopted by Nasim (2017) • IMDB -adopted by Jiang et al (2017) • AFINN -adopted by Jiang et al (2017) • DepecheMood Affective Lexicon (Staiano and Guerini, 2014) -adopted by Mansar et al (2017) • Amazon Product Reviews 16 -adopted by John and Vechtomova (2017) • Financial Phrasebank (Malo et al, 2014a) -adopted by John and Vechtomova (2017) • Corpus of Business News -adopted by Pivovarova et al (2017) In total, four lexica listed above are the ones mostly used (all by 4 participants each): (i) the Loughran and McDonald Sentiment Word, (ii) SentiWordNet, (iii) Opinion Lexicon and (iv) Harvard's General Inquirer Lexicon. Unlike the case in track 1, none of the participants ranked first till third used one of these four lexica.…”

Predicting Emotional Reaction in Social Networks

“…Whilst harnessing emotion rated content (e. g., news stories) like in [6,8], to learn word-emotion lexicons, we also go a step further and propose methods to adopt such lexicons for predicting emotion reactions towards emotional text (e. g., news posts). The task described in this work is thus inherently harder because of the latent factors that are implied in the process, e. g., a joyful news might be received with anger by a certain population if they already have a negative predisposition towards the entity concerned by the news, and inversely.…”

RoEmoLex - A Romanian Emotion Lexicon

“…Other resources are DepecheMood [10] and ANEW [2]. The first, DepecheMood, is a lexicon created in an entirely automated manner by using affective annotation provided implicitly by readers of articles on a news site.…”