Aspect-level sentiment analysis based on BERT fusion multi-attention

Xiao, Jing; Luo, Xingxian

doi:10.1109/ihmsc55436.2022.00016

Cited by 4 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this way, CNN can be used to analyze the important features in the sentence and the aspect emotion in the input sentence. 11) ASBABM [16]: The model implements the interaction between aspect words and context through aspect attention and self-attention mechanisms, and then the BiLSTM network enhances the dependency between sentences and aspect words.…”

Section: ) Capsar [12]: This Model Injects Aspect Information Into Th...mentioning

confidence: 99%

“…CNN over BERT-GCN [15] proposed to use the combination of BERT and BiLSTM to take into account the useful information hidden in context, and exploit the GCN model with multiple convolutional layers to capture the context features. ASBABM [16] proposed an aspect-level sentiment analysis model based on BERT and multi-level attention mechanisms. The model captures the interaction and correlation between the aspect word and the entire text through both the aspect attention mechanism and the selfattention mechanism.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

Guo

2023

IEEE Access

View full text Add to dashboard Cite

Aspect-level sentiment analysis is a fundamental task in sentiment analysis. It has many practical applications in product reviews, takeaway evaluations, and public opinion supervision. Over the past few years, previous studies have made remarkable progress. However, the existing methods still have some areas for improvement in aspect processing and feature fusion. In this study, we propose an aspectcontext dense concatenation model (ACDC) to merge deep semantic information from various aspects and contexts. ACDC first captures the interaction information between the aspect and context using an interaction matrix. Subsequently, a dense concatenation mechanism is used to extract and integrate features at different levels. In addition, we introduce two loss functions to eliminate the interference information and overfitting. On the Restaurant2014 and Laptop2014 datasets, the experimental results show that our proposed model achieves state-of-the-art performance in terms of accuracy and MF1. Furthermore, comparative experiments are conducted to verify the effectiveness of the proposed model.

show abstract

Section: ) Capsar [12]: This Model Injects Aspect Information Into Th...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

Guo

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

IIM: an information interaction mechanism for aspect-based sentiment analysis

Chen,

Ge,

Zhou

2023

Connection Science

View full text Add to dashboard Cite

Enhancing Minority Sentiment Classification in Gastronomy Tourism: A Hybrid Sentiment Analysis Framework With Data Augmentation, Feature Engineering and Business Intelligence

Razali,

Manaf,

Hanapi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The gastronomy tourism industry plays an important role in boosting local economies, enhancing the travel experience, and preserving culinary traditions unique to specific places. In this context, comprehending customer sentiments is of paramount importance for business decisions making, menu choices offering, marketing strategies, and customer service improvements. Traditional sentiment analysis methods in gastronomy tourism tend to be time-consuming, prone to human error, and influenced by subjectivity. Furthermore, the absence of an effective visualization strategy hampers the reliability of sentiment analysis efforts. Compounding this, the data collected often lacks balance across sentiment classes, making it challenging to predict minority sentiments accurately. To address these challenges, our research introduces a hybrid approach, combining various lexicon-based sentiment and emotional analysis algorithms, thereby enhancing the reliability of customer review analysis in the gastronomy tourism sector. Subsequently, we optimize machine learning sentiment classification by employing data augmentation in conjunction with feature engineering strategies, with the goal of improving the recognition of minority sentiment classes. Additionally, we present a comprehensive business intelligence and visualization solution that personalized for the gastronomy tourism industry in Sarawak that offering real-time sentiment visualization. The optimization of sentiment classification, achieved through the integration of synonym augmentation and ngram feature engineering in conjunction with kNN classifiers, has yielded impressive results. This approach attains optimal classification performance, boasting an accuracy rate of 0.98, an F1-score of 0.99, and a ROC-AUC score of 0.99. Notably, this methodology significantly enhances the recognition of minority sentiment classes within the dataset, addressing one of the main challenges in this research endeavor.INDEX TERMS business analytics, machine learning, sentiment analysis, tourism, visualization I. INTRODUCTION

show abstract

Aspect-level sentiment analysis based on BERT fusion multi-attention

Cited by 4 publications

References 5 publications

Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

Dense Concatenation Memory Network for Aspect Level Sentiment Analysis

IIM: an information interaction mechanism for aspect-based sentiment analysis

Enhancing Minority Sentiment Classification in Gastronomy Tourism: A Hybrid Sentiment Analysis Framework With Data Augmentation, Feature Engineering and Business Intelligence

Contact Info

Product

Resources

About