Deep learning for aspect extraction in textual opinions

Dionis López Ramos, Leticia Arco

Research output: Contribution to journalArticlepeer-review

Abstract

Aspect extraction in textual opinions is a very important task within the sentiment analysis or opinion mining, which allows achieving greater accuracy when analyzing information, and thus, contributing to decision making. Deep learning includes several algorithms or strategies that have obtained relevant results in various natural language processing tasks. There are several review papers on sentiment analysis that address deep learning as one of the existing techniques for extracting aspects; however, there are no review papers focus exclusively on to the use of deep learning in sentiment analysis. The main objective of this review paper is to offer a critical and comparative analysis of the main proposals and revision works that employ deep learning strategies for aspect extraction, by focusing on the representation approaches, principal models, obtained results and data sets used in experiments. In our proposal, the analysis of 53 papers published during the period 2011 to 2018 by highlighting their main successes, fissures, and research challenges is made. Finally, we propose some future research directions.
Translated title of the contributionDeep learning for aspect extraction in textual opinions
Original languageSpanish
Pages (from-to)105-145
Number of pages41
JournalRevista Cubana de Ciencias Informáticas
Volume13
Issue number2
Publication statusPublished - 3 Apr 2019

Keywords

  • opinion mining
  • aspect extraction
  • deep learning
  • natural language processing

Fingerprint

Dive into the research topics of 'Deep learning for aspect extraction in textual opinions'. Together they form a unique fingerprint.

Cite this