Research on Aspect-Level Sentiment Analysis Based on Text Comments

Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of t...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 14; no. 5; p. 1072
Main Authors Tian, Jing, Slamu, Wushour, Xu, Miaomiao, Xu, Chunbo, Wang, Xue
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2022
Subjects
Algorithms
aspect-based sentiment analysis
BERT
capsule network
Data mining
Datasets
Deep learning
Emotions
Feature extraction
Natural language processing
Neural networks
Semantics
SemEval 14 Restaurant Review dataset
Sentiment analysis
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Summary:Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA and ACSA are created. ATSA: Aspect Term Sentiment Analysis Dataset. ACSA: Aspect Category Sentiment Analysis Dataset. In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines capsule network and BRET, and proposes the baseline model CapsNet-BERT. The experimental results verify the effectiveness of the model.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14051072