47 citations · 239 across the 42 of their papers we have counts for
5 papers · 2 filters
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
Florian Strohm, Roman Klinger
The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aim…
IEST: WASSA-2018 Implicit Emotions Shared Task
Roman Klinger, Orphée De Clercq, Saif M. Mohammad +1
Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for…
A Survey on Sentiment and Emotion Analysis for Computational Literary Studies
Evgeny Kim, Roman Klinger
Emotions are a crucial part of compelling narratives: literature tells us about people with goals, desires, passions, and intentions. Emotion analysis is part of the broader and la…
Projecting Embeddings for Domain Adaptation: Joint Modeling of Sentiment Analysis in Diverse Domains
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Domain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to th…
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages
Jeremy Barnes, Roman Klinger, Sabine Schulte im Walde
Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide…