Dual-Channel Edge-Featured Graph Attention Networks for Aspect-Based Sentiment Analysis

Abstract

The goal of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information to assess the link between aspects and contextual words; nevertheless, most of this research has neglected phrases that are insensitive to syntactic analysis and the effect between various aspects in a sentence. In this paper, we propose a dual-channel edge-featured graph attention networks model (AS-EGAT), which builds an aspect syntactic graph by enhancing the contextual syntactic dependency representation of key aspect words and the mutual affective relationship between various aspects in the context and builds a semantic graph through the self-attention mechanism. We use the edge features as a significant factor to determine the weight coefficient of the attention mechanism to efficiently mine the edge features of the graph attention networks model (GAT). As a result, the model can connect important sentiment features of related aspects when dealing with aspects that lack obvious sentiment expressions, pay close attention to important word aspects when dealing with multiple-word aspects, and extract sentiment features from sentences that are not sensitive to syntactic dependency trees by looking at semantic features. Experimental results show that our proposed AS-EGAT model is superior to the current state-of-the-art baselines. Compared with the baseline models of LAP14, REST15, REST16, MAMS, T-shirt, and Television datasets, the accuracy of our AS-EGAT model increased by 0.76%, 0.29%, 0.05%, 0.15%, 0.22%, and 0.38%, respectively. The macro-f1 score increased by 1.16%, 1.16%, 1.23%, 0.37%, 0.53%, and 1.93% respectively.

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

“…Most of the studies worked on sentiment analysis [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 ], while study [ 81 ] worked on stance detection, study [ 82 ] focused on emotion recognition, study [ 83 ] focused on bias identification, and finally, study [ 84 ] focused on depression detection (Column C3). Unlike their singular focuses, our study’s paradigm (R19) was domain adaption; hence, our objective was threefold—sentimental analysis, and depression and suicide detection.…”

“…Unlike their singular focuses, our study’s paradigm (R19) was domain adaption; hence, our objective was threefold—sentimental analysis, and depression and suicide detection. Studies [ 67 , 68 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 80 , 81 , 83 , 85 ] used a single model as the base classifier (column C4), while only four studies [ 69 , 79 , 82 , 84 ] used HDL models as the base model. Notably, to outperform the existing results, our proposed study (R19) squarely used a hybrid of ALBERT, BERT, and BiLSTM as one of the base EDL models.…”

“…Ten out of the eighteen studies [ 67 , 69 , 72 , 73 , 74 , 76 , 77 , 80 , 83 , 84 ] used SDL models (columns C5, C6). In contrast, the authors of [ 68 , 70 , 71 , 75 , 78 , 79 , 81 , 82 ] and our proposed study (R19) used EDL models as the main architecture, using concatenation, fusion, or weighted sums. All of the studies [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ] demonstrated three to four kinds of data source, namely, social media chat, reviews, or public communications (column C9).…”

“…In contrast, the authors of [ 68 , 70 , 71 , 75 , 78 , 79 , 81 , 82 ] and our proposed study (R19) used EDL models as the main architecture, using concatenation, fusion, or weighted sums. All of the studies [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 ] demonstrated three to four kinds of data source, namely, social media chat, reviews, or public communications (column C9). Unlike these, in our proposed study (R19), keeping generalizability in mind, we used five kinds of data source, namely, Twitter, Twitter (Stanford), Reddit, Kaggle, and SemEval (benchmarking).…”

“…Furthermore, these eighteen studies, apart from [ 63 ], had a data size range of 1250 to 100,000 sentences (column C10), while in our proposed study (R19), the data size ranged from 17,719 to 1,600,000 sentences. Except for study [ 83 ], the remaining seventeen studies [ 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 84 ] computed at least one out of accuracy, F1-Score, or AUC (columns C11, C12). Our study (R19) outperformed the existing studies and yielded an accuracy of 93.97% and an F1-Score of 0.9192 .…”