29 citations · 145 across the 26 of their papers we have counts for
6 papers · 1 filter
A Fine-Grained Sentiment Dataset for Norwegian
Lilja Øvrelid, Petter Mæhlum, Jeremy Barnes +1
We introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying tex…
Embedding Projection for Targeted Cross-Lingual Sentiment: Model Comparisons and a Real-World Study
Jeremy Barnes, Roman Klinger
Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., Englis…
LTG-Oslo Hierarchical Multi-task Network: The importance of negation for document-level sentiment in Spanish
Jeremy Barnes
This paper details LTG-Oslo team's participation in the sentiment track of the NEGES 2019 evaluation campaign. We participated in the task with a hierarchical multi-task network, w…
On the Effect of Word Order on Cross-lingual Sentiment Analysis
Àlex R. Atrio, Toni Badia, Jeremy Barnes
Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent ne…
Sentiment analysis is not solved! Assessing and probing sentiment classification
Jeremy Barnes, Lilja Øvrelid, Erik Velldal
Neural methods for SA have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative diff…
Improving Sentiment Analysis with Multi-task Learning of Negation
Jeremy Barnes, Erik Velldal, Lilja Øvrelid
Sentiment analysis is directly affected by compositional phenomena in language that act on the prior polarity of the words and phrases found in the text. Negation is the most preva…