| Título |
IARM: Inter-aspect relation modeling with memory networks in aspect-based sentiment analysis |
| Tipo |
Congreso |
| Sub-tipo |
Memoria |
| Descripción |
2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 |
| Resumen |
Sentiment analysis has immense implications in modern businesses through user-feedback mining. Large product-based enterprises like Samsung and Apple make crucial business decisions based on the large quantity of user reviews and suggestions available in different e-commerce websites and social media platforms like Amazon and Facebook. Sentiment analysis caters to these needs by summarizing user sentiment behind a particular object. In this paper, we present a novel approach of incorporating the neighboring aspects related information into the sentiment classification of the target aspect using memory networks. Our method outperforms the state of the art by 1:6% on average in two distinct domains. |
| Observaciones |
|
| Lugar |
Bruselas |
| País |
Belgica |
| No. de páginas |
3402-3411 |
| Vol. / Cap. |
|
| Inicio |
2018-10-31 |
| Fin |
2018-11-04 |
| ISBN/ISSN |
9781948087841 |
