Cross-domain Sentiment Analysis Research Articles

A Comparative Analysis of Feature Selection Algorithms in Cross Domain Sentiment Classification

Background: Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. Method: The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. Results: From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Conclusion: Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.

Cross-Domain Aspect Based Sentiment Analysis

Abstract: Sentiment analysis, a crucial aspect of natural language processing, faces significant challenges in diverse domains. This research focuses on cross-domain aspect-based sentiment analysis, aiming to enhance accuracy across various fields. We propose a novel framework tailored for this purpose, addressing existing limitations. Through comprehensive evaluations on diverse datasets, our approach demonstrates improved efficacy compared to established models. The discussion highlights the strengths and limitations of our framework, contributing valuable insights to the field of sentiment analysis. Our research emphasizes the need for domain-specific considerations in sentiment analysis, offering practical implications for real-world applications. In conclusion, this study advances our understanding of cross-domain sentiment analysis challenges and provides a practical solution for improved accuracy in diverse domains

Investigating Semantic Differences in User-Generated Content by Cross-Domain Sentiment Analysis Means

Investigating Semantic Differences in User-Generated Content by Cross-Domain Sentiment Analysis Means

Combination of Chinese sentiment analysis datasets based on BiLSTM+Attention model

By training the Chinese sentiment analysis model, it is found that the prediction accuracy of the model trained by one dataset is obviously low on other datasets. Considering that the existing sentiment analysis work mainly uses a single domain corpus dataset and referring to the existing data processing methods on natural language processing, this paper designs an experiment to combine Chinese datasets from different fields into a large field-imbalanced dataset, and the number of samples from different fields in this dataset is obviously different. The new dataset is used to train a comprehensive Chinese sentiment analysis model and achieves satisfactory training results. According to the results of the experiments, the model trained by the field-imbalanced dataset has high prediction accuracy for samples from various fields, and the prediction accuracy increases with the increase of the proportion of corpus in this field in the training dataset. Through the experiment in this paper, some ideas are provided for the construction of large-scale cross-domain Chinese sentiment analysis datasets in the future.

Detecting Positive and Negative Deviations in Cross-Domain Product Reviews using Adaptive Stochastic Deep Networks

The analysis of sentiment in product reviews across diverse platforms such as e-commerce website and social media presents a challenging task due to the inherent differences in user behaviour and review formats. This research introduces an innovative methodology for detecting positive and negative deviations in cross-domain product reviews using Adaptive Stochastic Deep Networks (ASDN) tailored for multi-platform sentiment analysis. ASDNs possess mechanisms that enable dynamic adaptation to changes in data distributions, domain shifts, or varying complexities within the input data. The proposed framework aims to capture refined variations in sentiment expression across disparate platforms by incorporating adaptive stochasticity within deep neural networks. By adapting dynamically to changes in review styles, language use, and sentiment patterns unique to each platform, the ASDN architecture facilitates the identification of nuanced sentiment shifts. Through extensive experimentation on comprehensive datasets spanning Amazon, Facebook, and Instagram, the efficacy of the ASDN model in detecting positive and negative sentiment deviations across diverse platforms is demonstrated. This research contributes to advancing the understanding of sentiment dynamics across distinct social platforms and e-commerce sites, paving the way for more robust and adaptable models in cross-domain sentiment analysis.

A mutual mean teacher framework for cross-domain aspect-based sentiment analysis

A mutual mean teacher framework for cross-domain aspect-based sentiment analysis

Advances in Sentiment Analysis in Deep Learning Models and Techniques

The article investigates the advantages, disadvantages, and areas of research that need more exploration regarding deep learning architectures used in sentiment analysis. These architectures let models learn complex language features from data without explicit feature engineering, changing sentiment analysis. The models' capacity to capture long-range dependencies has improved their context and nuanced expression interpretation, especially in long or metaphorical texts. Deep learning sentiment analysis algorithms have improved, yet they still face obstacles. The complexity of these models raises ethical questions about bias and transparency. They also require huge, annotated datasets and computational resources, which limits their use in resource-constrained contexts. Adopting deep learning models requires balancing performance and practicality. Explore critical deep learning sentiment analysis research gaps. Cross-domain and cross-lingual sentiment analysis requires context- and language-specific models. Textual and non-textual multimodal sentiment analysis offers untapped potential for complex sentiment interpretation. Responsible AI deployment requires model interpretability, robustness against adversarial assaults, and domain consistency. Finally, deep learning and sentiment analysis have changed our knowledge of human emotion. Accuracy and contextual comprehension have improved, but model transparency, data prerequisites, and practical applicability remain issues. Overcoming these restrictions and exploring research gaps will enable responsible sentiment analysis AI innovation.

Soft prompt enhanced joint learning for cross-domain aspect-based sentiment analysis

Aspect term extraction is a fundamental task in fine-grained sentiment analysis, aiming to detect customer's opinion targets from reviews about products or services. The traditional supervised models have achieved promising results with annotated datasets. However, their performance dramatically decreases in cross-domain aspect term extraction tasks. Existing cross-domain transfer learning methods face two common limitations: (1) these works directly inject linguistic features into language models, making it challenging to transfer linguistic knowledge to the target domain; (2) they rely on the fixed predefined prompts, which is time-consuming to construct the prompts for all potential aspect term spans. To address the limitations, we propose a soft prompt-based joint learning method for cross-domain aspect term extraction in this paper. Specifically, by incorporating external linguistic features, the proposed method learns domain-invariant representations between source and target domains via multiple objectives, which bridges the gap between domains with varied distributions of aspect terms. Furthermore, the proposed method interpolates a set of transferable soft prompts consisting of multiple learnable vectors that are beneficial to detect aspect terms in the target domain. Extensive experiments are conducted on two groups of datasets and the experimental results demonstrate the effectiveness of the proposed method for cross-domain aspect terms extraction.

Cross-Domain Aspect-Based Sentiment Classification by Exploiting Domain- Invariant Semantic-Primary Feature

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this paper, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

Learning Disentangled Representation for Multimodal Cross-Domain Sentiment Analysis.

Multimodal cross-domain sentiment analysis aims at transferring domain-invariant sentiment information across datasets to address the insufficiency of labeled data. Existing adaptation methods achieve well performance by remitting the discrepancies in characteristics of multiple modalities. However, the expressive styles of different datasets also contain domain-specific information, which hinders the adaptation performance. In this article, we propose a disentangled sentiment representation adversarial network (DiSRAN) to reduce the domain shift of expressive styles for multimodal cross-domain sentiment analysis. Specifically, we first align the multiple modalities and obtain the joint representation through a cross-modality attention layer. Then, we disentangle sentiment information from the multimodal joint representation that contains domain-specific expressive style by adversarial training. The obtained sentiment representation is domain-invariant, which can better facilitate the sentiment information transfer between different domains. Experimental results on two multimodal cross-domain sentiment analysis tasks demonstrate that the proposed method performs favorably against state-of-the-art approaches.

An improved unified domain adversarial category-wise alignment network for unsupervised cross-domain sentiment classification

Cross-domain sentiment analysis (CDSA) aims to learn transferable knowledge from the source domain to facilitate the sentiment polarity classification on the target domain of lacking labeled data. Currently, two types of unsupervised domain adaptation (UDA) methods are widely used in CDSA tasks. One employs the domain adversarial strategy to extract domain-invariant features, and the other utilizes the distance metric strategy to reduce domain distribution discrepancy. However, the fine-grained domain-specific information related to categories aligned between domains is not preserved, which suppresses the performance of target-domain classification. To overcome the mentioned problem, a unified Domain Adversarial Category-wise Alignment Network (DACAN) was proposed in this paper. An integrated network was constructed with progressive multi-level feature learning. Specifically, a feature extraction module was constructed with parameter sharing between two domains at low-level text feature extraction layers. The domain adversarial module was added to enable shared knowledge transfer by extracting domain-invariant information and by updating the shared parameters at the feature extraction layers. A category-wise alignment module was built to achieve local distribution alignment at the high dimension-level semantic layers guided by fine-grained category structure information. Meanwhile, joint constraint was established with domain-invariant constraint based on domain adversarial, and domain-consistency constraint based on category-wise alignment. Comprehensive experiments were conducted on two standard Amazon review datasets. The results show that DACAN outperforms other state-of-the-art UDA methods by 0.7% and 1.1% on the two- and three-category CDSA tasks, respectively. Also, better performance results are achieved with a synergistic UDA scheme compared with a single UDA scheme.

Cross-Domain Sentiment Analysis Based on Feature Projection and Multi-Source Attention in IoT.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

An efficient LSTM based cross domain aspect based sentiment analysis (CD-ABSA)

This research study focuses the cross-domain aspect-based sentiment analysis (CD-ABSA) for existing source domain annotation data. The CD-ABSA tries to use the valuable information in a source domain to extract aspect terms and evaluate their sentiment polarities in a target domain. It can considerably increase the usage of the source domain annotation resources while also reducing the workload of newer domain data annotation. one of the main components of the CD-ABSA is aspect extraction. In this paper, we utilized the most common topic modelling techniques: LDA and LSA to extract aspects from the reviews as it does not require labelled data. The topics are extracted from the education domain of the Course and Teacher Performance Evaluation (CTPE) dataset. In this paper, we also evaluated the different hyper-parameters on the CD-ABSA model and selected the best and optimal combination. The proposed methodology train on domain-dependent and independent word embedding that achieves CD-ABSA, in particularly end-to-end fashion. The experiment carries out on Academica dataset, which consists of students’ comments/feedback and SemEval-2014 dataset, which includes laptops and restaurants reviews. The evaluation metrics such as (precision, recall, F1 score and validation Accuracy) is considered while judging the LSTM classifier performance for CD-ABSA as a result.

Global-Local Dynamic Adversarial Learning for Cross-Domain Sentiment Analysis

As one of the most widely used applications in domain adaption (DA), Cross-domain sentiment analysis (CDSA) aims to tackle the barrier of lacking in sentiment labeled data. Applying an adversarial network to DA to reduce the distribution discrepancy between source and target domains is a significant advance in CDSA. This adversarial DA paradigm utilizes a single global domain discriminator or a series of local domain discriminators to reduce marginal or conditional probability distribution discrepancies. In general, each discrepancy has a different effect on domain adaption. However, the existing CDSA algorithms ignore this point. Therefore, in this paper, we propose an effective, novel and unsupervised adversarial DA paradigm, Global-Local Dynamic Adversarial Learning (GLDAL). This paradigm is able to quantitively evaluate the weights of global distribution and every local distribution. We also study how to apply GLDAL to CDSA. As GLDAL can effectively reduce the distribution discrepancy between domains, it performs well in a series of CDSA experiments and achieves improvements in classification accuracy compared to similar methods. The effectiveness of each component is demonstrated through ablation experiments on different parts and a quantitative analysis of the dynamic factor. Overall, this approach achieves the desired DA effect with domain shifts.

Counterfactual Representation Augmentation for Cross-Domain Sentiment Analysis

Cross-domain sentiment analysis aims to adapt sentiment classification models trained on one or more source domains to the target domain, which can effectively alleviate the problem of insufficient labelled data in specific domains. Unlike most previous methods of adjusting models based on observed data, we propose a novel counterfactual representation augmentation (CRA) method, which aims to improve target-domain generalization by constructing new counterfactual representations for training. Specifically, we train a domain discriminator to learn the domain discrepancy between the source and target domains on unlabeled data, and use a gradient editing method to directly construct counterfactual representations, which reduces the inductive bias of the source domain and augments the training data. Moreover, we further leverage an ensemble-based training method to indirectly encourage the target-domain classifier to rely more on robust features for prediction. Extensive experiments on a widely-used cross-domain sentiment classification benchmark dataset show that our method consistently surpasses different baseline methods on different tasks, demonstrating the strong ability to improve domain generalization. We also find that our model can effectively adjust the decision boundary to make the classifier more robust and generalized, through extensive qualitative and quantitative analysis.

Moka-ADA: adversarial domain adaptation with model-oriented knowledge adaptation for cross-domain sentiment analysis

Moka-ADA: adversarial domain adaptation with model-oriented knowledge adaptation for cross-domain sentiment analysis

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Sentiment Analysis task helps us to estimate the opinion of a person from his reviews or comments about a product, person, politics, etc., Cross-Domain Sentiment Analysis (CDSA) empowers the Sentiment models with the ability to forecast the opinion of a review coming from a different domain other than the domain where the model is trained. The challenge of the CDSA model relies on bridging the relationship between words in the source and target domain. Several types of research in CDSA focus on determining the domain invariant features to adapt the model to the target domain, such model shows less focus on aspect terms of the sentence. We propose CWAN (Collaborative Word Attention Network), which integrates aspects and domain invariant features of the sentences to calculate the sentiment. CWAN uses attention networks to capture the domain-independent features and aspects of the sentences. The sentence and aspect attention models are executed collaboratively to determine the sentiment of the sentence. Amazon product review dataset is used in this experiment. The performance of the CWAN model is compared with other baseline CDSA models. The results show that CWAN outperforms other baseline models.

Cross-Domain Sentiment Analysis Based on Small in-Domain Fine-Tuning

Significant progress has been made in sentiment analysis over the past few years, especially due to the application of deep neural language models. However, there is a problem of transferability of trained models from one domain to another, especially for less studied languages such as Russian. We propose an approach to build cross-domain sentiment analysis models based on a two-stage procedure: first, we fine-tune a pre-trained RuBERT language model on a combined non-domain corpus, and then fine-tune this model on a small domain corpus. We conducted large-scale experiments with 30 sentiment annotated corpora across 12 domains. In order to increase the representativeness of news texts with high-quality annotation, we created a novel RuNews corpus, containing 1,823 news articles annotated by sentiment. The results show that fine-tuning the model using a small number (about several hundred) of annotated domain texts can significantly improve the performance of sentiment analysis for a new domain (on average by 4.6 p.p.). We also obtained the state-of-the-art results for 7 out of 14 test corpora.

Aspect-Opinion Correlation Aware and Knowledge-Expansion Few Shot Cross-Domain Sentiment Classification

Cross-domain sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, most of the existing cross-domain sentiment classification models neglect the domain-specific features, which limits their performance especially when the domain discrepancy becomes larger. Meanwhile, the relations between the aspect and opinion terms cannot be effectively modeled and thus the sentiment transfer error problem is suffered in the existing unsupervised domain-adaptation methods. To address these two issues, we propose an aspect-opinion correlation aware and knowledge-expansion few shot cross-domain sentiment classification model. Sentiment classification can be effectively conducted with only a few support instances of the target domain. Extensive experiments are conducted and the experimental results show the effectiveness of our proposed model.

Aspect-Opinion Sentiment Alignment for Cross-Domain Sentiment Analysis (Student Abstract)

Cross-domain sentiment analysis (SA) has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, exiting unsupervised cross-domain SA models ignore the relation between the aspect and opinion, which suffer from the sentiment transfer error problem. To solve this problem, we propose an aspect-opinion sentiment alignment SA model and extensive experiments are conducted to evaluate the effectiveness of our model.