Sentiment Analysis with Contextual Embeddings and Self-Attention
Computation and Language
2020-10-07 v2 Artificial Intelligence
Machine Learning
Abstract
In natural language the intended meaning of a word or phrase is often implicit and depends on the context. In this work, we propose a simple yet effective method for sentiment analysis using contextual embeddings and a self-attention mechanism. The experimental results for three languages, including morphologically rich Polish and German, show that our model is comparable to or even outperforms state-of-the-art models. In all cases the superiority of models leveraging contextual embeddings is demonstrated. Finally, this work is intended as a step towards introducing a universal, multilingual sentiment classifier.
Cite
@article{arxiv.2003.05574,
title = {Sentiment Analysis with Contextual Embeddings and Self-Attention},
author = {Katarzyna Biesialska and Magdalena Biesialska and Henryk Rybinski},
journal= {arXiv preprint arXiv:2003.05574},
year = {2020}
}
Comments
Accepted at the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS 2020)