Deep Mask Memory Network with Semantic Dependency and Context Momentfor Aspect Level Sentiment Classification
Abstract
Aspect level sentiment classification aims at identifying the sentiment of each aspect term in a sentence. Deep memory networks often use location
information between context word and aspect to
generate the memory. Although improved results
are achieved, the relation information among aspects in the same sentence is ignored and the word
location can’t bring enough and accurate information for the analysis on the aspect sentiment. In this
paper, we propose a novel framework for aspect
level sentiment classification, deep mask memory network with semantic dependency and context moment (DMMN-SDCM), which integrates
semantic parsing information of the aspect and the
inter-aspect relation information into deep memory
network. With the designed attention mechanism
based on semantic dependency information, different parts of the context memory in different computational layers are selected and useful inter-aspect
information in the same sentence is exploited for
the desired aspect. To make full use of the interaspect relation information, we also jointly learn a
context moment learning task, which aims to learn
the sentiment distribution of the entire sentence for
providing a background for the desired aspect. We
examined the merit of our model on SemEval 2014
Datasets, and the experimental results show that our
model achieves a state-of-the-art performance