Fine-grained Sentiment Analysis Task Research Articles

Aspect-level multimodal sentiment analysis based on co-attention fusion

AbstractAspect-level multimodal sentiment analysis is the fine-grained sentiment analysis task of predicting the sentiment polarity of given aspects in multimodal data. Most existing multimodal sentiment analysis approaches focus on mining and fusing multimodal global features, overlooking the correlation of more fine-grained multimodal local features, which considerably limits the semantic relevance between different modalities. Therefore, a novel aspect-level multimodal sentiment analysis method based on global–local features fusion with co-attention (GLFFCA) is proposed to comprehensively explore multimodal associations from both global and local perspectives. Specially, an aspect-guided global co-attention module is designed to capture aspect-guided intra-modality global correlations. Meanwhile, a gated local co-attention module is introduced to capture the adaptive association alignment of multimodal local features. Following that, a global–local multimodal feature fusion module is constructed to integrate global–local multimodal features in a hierarchical manner. Extensive experiments on the Twitter-2015 dataset and Twitter-2017 dataset validate the effectiveness of the proposed method, which can achieve better aspect-level multimodal sentiment analysis performance compared with other related methods.

PSAN: Prompt Semantic Augmented Network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task. The model is influenced by irrelevant context and complex structure when modeling the relationship between aspect and sentiment expression. Existing studies mainly improve the performance of the ABSA task by fine-tuning the pre-trained language model(PLM) and extracting valid information(e.g., syntax and context). However, these approaches fail to exploit the performance of PLM and ignore the rich semantic knowledge contained in PLM. To alleviate these problems, we propose a Prompt Semantic Augmented Network (PSAN), which improves the performance of ABSA by fine-tuning the PLM and leveraging the semantic knowledge contained in the PLM. Specifically, we propose semantic augmented prompt tuning (SAPT), which obtains a more relevant representation of the task by constructing prompt templates for the input sentences. Meanwhile, we construct corresponding label templates for the prompt templates and use them as the labels of the prompt templates, by which the sentence information containing the real labels is introduced into the model as a prior knowledge. Meanwhile, we use self-attention and graph convolutional networks (GCN) to obtain contextual and syntactic information. And finally, we aggregate these three types of information to perform the ABSA task. Experiments show that our SAPT can improve the performance of the PLM compared to classical fine-tuning, resulting in better results. Our proposed SAPT can obtain richer prompt semantic information than prompt tuning. Experimental results on three public benchmark datasets show that our model outperforms state-of-the-art methods.

Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model.

A Cross-Domain Generative Data Augmentation Framework for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a crucial fine-grained sentiment analysis task that aims to determine sentiment polarity in a specific aspect term. Recent research has advanced prediction accuracy by pre-training models on ABSA tasks. However, due to the lack of fine-grained data, those models cannot be trained effectively. In this paper, we propose the cross-domain generative data augmentation framework (CDGDA) that utilizes a generation model to produce in-domain, fine-grained sentences by learning from similar, coarse-grained datasets out-of-domain. To generate fine-grained sentences, we guide the generation model using two prompt methods: the aspect replacement and the aspect–sentiment pair replacement. We also refine the quality of generated sentences by an entropy minimization filter. Experimental results on three public datasets show that our framework outperforms most baseline methods and other data augmentation methods, thereby demonstrating its efficacy.

Fusing sentiment knowledge and inter-aspect dependency based on gated mechanism for aspect-level sentiment classification

Aspect level sentiment classification is a fine-grained sentiment analysis task that aims to identify the sentiment polarity of one or more given aspects in a sentence. In natural language, words frequently carry certain sentimental tendencies, which can be beneficial in obtaining the features between aspects and contexts. On the other hand, the dependencies between different aspects in a sentence can provide sufficient information for the sentiment polarity discrimination of a target aspect. However, existing models tend to focus on sentiment knowledge or aspect interactions individually without leveraging their converged information. Therefore, we propose a model based on Gated Mechanism Fusing Sentiment Knowledge and Inter-Aspect dependency (GMF-SKIA) for Aspect-level Sentiment Classification in this paper, aiming to dynamically fuse sentiment knowledge information of words and inter-aspect dependency. Specifically, the model uses the SenticNet sentiment dictionary to add sentiment knowledge information to words during dependency tree construction, and then we introduce a graph convolutional network to obtain sentiment information of dependency tree. We utilize an aspect-related multiheaded self-attention mechanism to model the inter-aspect interactions. Moreover, we design an information gate based on gated mechanism to fuse sentiment knowledge and inter-aspect features. We performed experiments on four publicly available datasets, our model outperforms the best benchmark model by an average of 2.1% and achieves the highest accuracy of 91.56% on the Rest16 dataset.

Improving aspect-based sentiment analysis with contrastive learning

As a fine-grained sentiment analysis task focusing on detecting the sentiment polarity of aspect(s) in a sentence, aspect-based sentiment analysis (ABSA) plays a significant role in opinion analysis and review analysis. Recently, a number of methods have emerged to leverage contrastive learning techniques to enhance the performance of ABSA by learning fine-grained sentiment representations. In this paper, we present and compare two commonly used contrastive learning approaches for enhancing ABSA performance: sentiment-based supervised contrastive learning and augmentation-based unsupervised contrastive learning. Sentiment-based supervised contrastive learning employs sentiment labels to distinguish between positive and negative samples. Augmentation-based unsupervised contrastive learning aims to utilize various data augmentation strategies to generate positive samples. Experimental results on three public ABSA datasets demonstrate that both contrastive learning methods significantly improve the performance of ABSA. Sentiment-based supervised contrastive learning outperforms augmentation-based unsupervised contrastive learning in terms of overall performance improvements. Furthermore, we conduct additional experiments to illustrate the effectiveness and generalizability of these two contrastive learning approaches. The experimental code and data are publicly available at the link: https://github.com/Linda230/ABSA-CL.

Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a fine-grained sentiment analysis task that consists of two types of subtasks: aspect term extraction and aspect sentiment classification. In the aspect term extraction task, current methods suffer from the lack of fine-grained information in aspect term extraction and difficulty in identifying aspect term boundaries. In the aspect sentiment classification task, the current aspect sentiment classifier cannot adapt itself to the text and determine the local context. To address these two challenges, this work proposes an adaptive semantic relative distance approach based on dependent syntactic analysis, which uses adaptive semantic relative distance to determine the appropriate local context for each text and increase the accuracy of sentiment analysis. Meanwhile, the study also predicts the current word labels by combining local information features extracted by local convolutional neural networks and global information features to precisely locate the word labels. In two subtasks, our proposed model improves accuracy and F1 scores on the SemEval-2014 Task 4 Restaurant and Laptop datasets compared to the state-to-the-art approaches, especially in the aspect sentiment classification subtask.

Aspect-Level Sentiment Classification Based on Auto-Adaptive Model Transfer

Aspect-level sentiment classification (ASC) is a fine-grained sentiment analysis task that involves detecting the sentiment polarity of a specific opinion target in a given sentence. Despite the popularity of deep learning methods, the limited availability of ASC training data has resulted in the suboptimal performance of neural network models. To address this issue, researchers have proposed transferring resource-rich document-level sentiment knowledge to ASC tasks using model transfer and shared parameters. However, these methods have neglected the impact of differences in the training data for different tasks on the shared layer parameters. Therefore, this paper proposes a transfer method that automatically adapts to task and domain differences—Auto-adaptive Model Transfer (AAMT). This method considers adding specific disturbance variables on shared layer parameters to subdivide task differences to further obtain more accurate and effective transfer knowledge. In addition, on this basis, we introduce an attention mechanism to combine aspect and sentence features to better capture the sentiment vocabulary associated with a given target. A series of experiments on three public datasets show that our proposed method can solve the problem of insufficient training data for ASC and achieve good results.

RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.

User preference mining based on fine-grained sentiment analysis

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.

Syntactic Knowledge Embedding Network for Aspect-Based Sentiment Classification

An aspect-based sentiment classification task is a fine-grained sentiment analysis task, which is aimed at identifying the sentiment polarity of a given aspect in subjective sentences. In recent years, some researchers have applied pretrained BERT models to this task. However, existing research only uses the BERT output layer and ignores the syntactic features in the middle layers, leading to deviations in the prediction results. In order to solve above problems, we propose a new model BERT-SFE. Firstly, we explicitly utilize the middle layers of BERT to capture the underlying syntactic features. Secondly, we construct a syntactic feature extraction unit based on Star-Transformer, which uses an auxiliary vector and the star network structure to capture both local and global syntactic information in a sentence. Finally, we merge the syntactic features with the semantic features from the BERT output layer in the feature fusion layer, obtaining a more accurate sentiment representation of the aspect. The experimental results on three public ABSA datasets show that using the syntactic feature extraction unit based on Star-Transformer to mine the syntactic knowledge in the middle layers of BERT can effectively improve the accuracy of sentiment classification. BERT-SFE achieves the best performance compared with existing models.

Aspect-level sentiment classification based on attention-BiLSTM model and transfer learning

Aspect-level sentiment classification, a fine-grained sentiment analysis task which provides entire and intensive results, has been a research focus in recent years. However, the performance of neural network models is largely limited by the small scale of datasets for aspect-level sentiment classification due to the challenges to label such data. In this paper, we propose an aspect-level sentiment classification model based on Attention-Bidirectional Long Short-Term Memory (Attention-BiLSTM) model and transfer learning. Based on Attention-BiLSTM model, three models including Pre-training (PRET), Multitask learning (MTL), and Pre-training & Multitask learning (PRET+MTL) are proposed to transfer the knowledge obtained from document-level training of sentiment classification to aspect-level sentiment classification. Finally, the performance of the four models is verified on four datasets. Experiments show that proposed methods make up for the shortcomings of poor training of neural network models due to the small dataset of the aspect-level sentiment classification.

Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks

Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.

Sentiment classification based on dependency-relationship embedding and attention mechanism

Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM.

Multiple-element joint detection for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to detect target-aspect-sentiment elements in a sentence. Most of the existing research work distinguished the sentiment for aspects or targets independently, ignoring the corresponding relation between the targets and the aspects. However, such a corresponding relation is significant for the accurate prediction of fine-grained sentiment polarity. In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence. Our model utilizes BERT to obtain the initial embedding vector from the aspect–sentence joint input and applies bidirectional long short-term memory to model aspect and sentence representations. We then employ a graph convolutional network with attention mechanisms to capture the dependency relationship between aspect and sentence. We evaluate our approach on two restaurant datasets of SemEval 2015 Task 12 and SemEval 2016 Task 5. Experiment results show that our model achieves state-of-the-art performance in extracting (target, aspect, sentiment) triples. Moreover, the model also has good performance on multiple subtasks of target-aspect-sentiment detection.

Residual attention and other aspects module for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose the original information. Besides, in the multi-aspect text, the sentiment information of other aspect terms interferes with the sentiment analysis of the current aspect term likely. In this paper, we propose two models named RA-CNN and RAO-CNN for ABSA tasks. In RA-CNN, we apply CNN to model the aspect term and utilize a specially designed residual attention mechanism to interact with the text. Based on the RA-CNN, RAO-CNN adds other aspect terms module, which can reduce interference of sentiment information related to other aspect terms in the multi-aspect text. To verify the proposed models’ effectiveness, we conduct a large number of experiments and comparisons on seven public datasets. Experimental results show that our proposed models are useful and achieve state-of-the-art results.

Deep Interactive Memory Network for Aspect-Level Sentiment Analysis

The goal of aspect-level sentiment analysis is to identify the sentiment polarity of a specific opinion target expressed; it is a fine-grained sentiment analysis task. Most of the existing works study how to better use the target information to model the sentence without using the interactive information between the sentence and target. In this article, we argue that the prediction of aspect-level sentiment polarity depends on both context and target. First, we propose a new model based on LSTM and the attention mechanism to predict the sentiment of each target in the review, the matrix-interactive attention network (M-IAN) that models target and context, respectively. M-IAN use an attention matrix to learn the interactive attention of context and target and generates the final representations of target and context. Then we introduce two gate networks based on M-IAN to build a deep interactive memory network to capture multiple interactions of target and context. The deep interactive memory network can excellently formulate specific memory for different targets, which is helpful in sentiment analysis. The experimental results of Restaurant and Laptop datasets of SemEval 2014 validate the effectiveness of our model.

Aspect-Level Drug Reviews Sentiment Analysis Based on Double BiGRU and Knowledge Transfer

Aspect-level sentiment analysis is a fine-grained sentiment analysis task designed to identify the sentiment polarity of specific target in a sentence. However, this task is rarely used in drug reviews. Some models for this task ignore the impact of target semantics, and others do not perform well because the datasets are relatively smaller. Therefore, we propose a Pretraining and Multi-task learning model based on Double BiGRU (PM-DBiGRU). In PM-DBiGRU, we first use the pretrained weight learned from short text-level drug review sentiment classification task to initialize related weight of our model. Then two BiGRU networks are applied to generate the bidirectional semantic representations of the target and drug review, and attention mechanism is used to obtain the target-specific representation for aspect-level drug review. The multi-task learning is further utilized to transfer the helpful domain knowledge from the short text-level drug review corpus. We also propose a dataset SentiDrugs for aspect-level drug review sentiment classification, in which each review may contain one or more targets. Experimental results on SentiDrugs demonstrate that our approach can improve the performance of aspect-level drug reviews sentiment classification compared with other state-of-the-art architectures.

Improving aspect-based sentiment analysis via aligning aspect embedding

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to predict sentiment polarities of given aspects or target terms in text. ABSA contains two subtasks: Aspect-Category Sentiment Analysis (ACSA) and Aspect-Term Sentiment Analysis (ATSA). Aspect embeddings have been extensively used for representing aspect-categories on ACSA task. Based on our observations, existing aspect embeddings cannot properly represent the relation between aspect-categories and aspect-terms. To address this limitation, this paper presents a learning method which trains aspect embeddings according to the relation between aspect-categories and aspect-terms. According to the cosine measure metric we proposed in this paper, the limitation is successfully alleviated in the aspect embeddings which are trained by our method. The trained aspect embeddings can be used as initialization in existing models to solve ACSA task. We conduct experiments on SemEval datasets for ACSA task, and the results indicate that our pre-trained aspect embeddings are capable of improving the performance of sentiment analysis.

Semi-supervised Aspect-level Sentiment Classification Model based on Variational Autoencoder

Aspect-level sentiment classification aims to predict the sentiment of a text in different aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention-based Long Short-Term Memory Network to perform aspect-level sentiment classification. Most previous work are based on supervised learning that needs a large number of labeled samples, but the problem is that only a limited subset of data samples are labeled in practical applications. To solve this problem, we propose a novel Semi-supervised Aspect Level Sentiment Classification Model based on Variational Autoencoder (AL-SSVAE) for semi-supervised learning in the aspect-level sentiment classification. The AL-SSVAE model inputs a given aspect to an encoder a decoder based on a variational autoencoder (VAE), and it also has an aspect level sentiment classifier. It enables the attention mechanism to deal with different parts of a text when different aspects are taken as input as previous methods. Due to that the sentiment polarity of a word is usually sensitive to the given aspect, a single vector for a word is problematic. Therefore, we propose the aspect-specific word embedding learning from a topical word embeddings model to express a word and also append the corresponding sentiment vector into the word input vector. We compare our AL-SSVAE model with several recent aspect-level sentiment classification models on the SemEval 2016 dataset. The experimental results indicate that the proposed model is able to capture more accurate semantics and sentiment for the given aspect and obtain better performance on the task of the aspect level sentiment classification. Moreover, the AL-SSVAE model is able to learn with the semi-supervised mode in the aspect level sentiment classification, which enables it to learn efficiently using less labeled data.