Deep Mask Memory Network with Semantic Dependency and Context Moment for 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 inter-aspect 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.

Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification

“…DMMN-SDCM is based on a memory network and introduces semantic analysis information to guide the attention mechanism to build a Deep Mask Memory Network (DMMK) as well as effectively learn the information provided by other non-target aspects. At the same time, the context moment proposed by the model is embedded in the sentiment classification of the entire sentence and is designed to provide background information for the target aspect [47].…”

Inducing Target-Specific Latent Structures for Aspect Sentiment Classification

“…Recently, there is a line of work considering dependency tree information for ATSC. Lin et al (2019) proposed deep mask memory network based on dependency trees. and Sun et al (2019b) encoded dependency tree using GCNs for aspect-level sentiment analysis.…”

Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification

“…However, most of the previous works neglect the power of syntax information. Syntactic-based methods (Sun et al, 2019;Zhao et al, 2019;Huang and Carley, 2019;Lin et al, 2019) introduce the results of dependency parsing into the DNN models to shorten the distance between the aspect and the keyword and introduce syntactic information. DNN-based models including semantic-based and syntactic-based methods can generate dense vectors of sentences without handcrafted features.…”