A BERT-Based Aspect-Level Sentiment Analysis Algorithm for Cross-Domain Text

Abstract

Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance.

A Survey of Sentiment Analysis and Sarcasm Detection

“…Liu et al [13] proposed an algorithmic framework for cross-domain aspect-level sentiment analysis using the Amazon product review dataset, which comprises reviews for products across five domains: Books, DVD disks, Electronics, Kitchen appliances, and Videos. The proposed algorithmic framework utilizes the convolutional, adversarial, and BERT models.…”

“…Sentiment analysis, situated at the intersections of computational linguistics, NLP, and data mining, utilizes techniques from these fields to extract sentiments from text. It explores research areas such as data mining and machine learning within the context of NLP [12,13]. Sarcasm [14][15][16][17][18], a statement conveying the as it can invert the true sentiment of a statement.…”

Systematic Review on Aspect-Based SentimentAnalysis in Cross-Domain

“…This way, the model learns to classify sentiments with source domain data using invariant features. Liu and Zhao (2022) created an aspect classification model. The data passes through BERT.…”

Sentiment Analysis of Online New Energy Vehicle Reviews

“…In the equations [43], TP stands for true positives, which are the samples predicted as class A and are actually class A. FP represents false positives, which are the samples predicted as class A but are not actually class A. TN represents true negatives, which are the samples predicted as not class A and are actually not class A. FN stands for false negatives, which are the samples predicted as not class A but are actually class A.…”