Cross-domain Sentiment Analysis Based On Pre-training And Adversarial Learning

Sentiment analysis is crucial for extracting emotional tendencies,opinions,and views from subjective text,and it plays a vital role in market research,consumer decision-making,personalized recommendations,and public opinion analysis.However,traditional sentiment analysis methods often suffer from poor performance due to domain-specificity and data scarcity in certain domains.To address these challenges,crossdomain sentiment analysis has emerged as a research focus.This thesis aims to tackle three core challenges in cross-domain sentiment analysis:domain adaptation,sample selection,and knowledge transfer strategy.The objective is to develop a model with strong generalization ability,high accuracy,and robustness to effectively perform sentiment analysis in data-scarce domains.Existing research methods face unresolved issues,including inefficient representation learning in sentiment feature extraction,insufficient robustness in complex domain feature alignment,and excessive redundancy in domain fusion as the number of source domains increases.To overcome these problems,this thesis proposes a novel multisource cross-domain sentiment analysis method that leverages deep learning models,adversarial learning ideas,and multi-task learning mechanisms.The research work encompasses three main parts:First,to enhance the efficiency of representation learning for domain-shared sentiment features,this thesis introduces a text feature extraction method based on emotion perception pre-training.This approach utilizes a language model pre-trained on a domain-shared sentiment dictionary and a large-scale comment corpus to generate context-related word vectors.This strategy enables the learning of deep-level,emotionrelated semantic information that is not specific to any particular domain.Additionally,the method incorporates a "shared-private" parameter multitask learning mechanism to more effectively differentiate domain-shared and private features.Second,to improve the domain discrimination ability of decision boundary fuzzy sentiment features,this thesis proposes a domain feature alignment method based on domain difference measurement and adversarial learning.A novel data distribution distance calculation tool is defined to quantify the differences between different domains,which are then minimized to achieve domain feature alignment.Furthermore,a domain discriminator is introduced to perform adversarial training,minimizing the discriminator loss function and screening shared sentiment expression features between the source and target domains.Third,to optimize the contribution ratios of various source domains in multi-source domain scenarios,this thesis presents a domainconfidence-based multi-source domain fusion method.Building upon the previous two research components,an end-to-end multi-source crossdomain sentiment analysis model is proposed.This model effectively extracts shared emotional knowledge for the target domain through emotion perception pre-training,domain difference measurement,and adversarial learning.Moreover,it automatically learns the "confidence level" of each source domain for the target domain,adjusts the contribution ratio for multi-source domain fusion,and flexibly adapts to multi-source crossdomain sentiment analysis tasks in diverse scenarios.To validate the effectiveness and progressiveness of the proposed method,this thesis conducts a comprehensive set of validation experiments and comparative analyses.The experimental results demonstrate that the proposed method exhibits outstanding performance and significantly enhances the accuracy of Chinese cross-domain sentiment analysis.